Download "Encontro On-line Matemática aplicada a computação"

Please wait. We're preparing links for easy ad-free video watching and downloading.

Previous video: Engage Students with Classroom "Paddle" Props // Kids English Theatre Next video: 58°(3 parte) LECCION DE TALLA HIJOS DE ESTEBAN JIMENEZ

58°(3 parte) LECCION DE TALLA HIJOS DE ESTEBAN JIMENEZ

58°(3 parte) LECCION DE TALLA HIJOS DE ESTEBAN JIMENEZ

Channel: Tallista Esteban Jiménez Escribano

Engage Students with Classroom "Paddle" Props // Kids English Theatre

Engage Students with Classroom "Paddle" Props // Kids English Theatre

Channel: Kids English Theatre - How to Teach ESL Drama

НЕПРАВИЛЬНЫЕ ГЛАГОЛЫ АНГЛИЙСКОГО ЯЗЫКА ПРАКТИКА АНГЛИЙСКИЙ ЯЗЫК УРОК 5 АНГЛИЙСКИЙ ДЛЯ НАЧИНАЮЩИХ

НЕПРАВИЛЬНЫЕ ГЛАГОЛЫ АНГЛИЙСКОГО ЯЗЫКА ПРАКТИКА АНГЛИЙСКИЙ ЯЗЫК УРОК 5 АНГЛИЙСКИЙ ДЛЯ НАЧИНАЮЩИХ

Channel: АНГЛИЙСКИЙ ЯЗЫК ПО ПЛЕЙЛИСТАМ

Medidas de dispersão

Medidas de dispersão

Channel: PUCPR Online

The Victims of Benny Hinn: 30 Years of Spiritual Deception.

The Victims of Benny Hinn: 30 Years of Spiritual Deception.

Channel: Mike Winger

Урок 5 | Unreal Engine 4 Искусственный интеллект - Преодоление препятствий ИИ | AI Nav Link Proxy

Урок 5 | Unreal Engine 4 Искусственный интеллект - Преодоление препятствий ИИ | AI Nav Link Proxy

Channel: Cyberstars - How to create a game

Воздух. Состав воздуха. 7 класс.

Воздух. Состав воздуха. 7 класс.

Channel: МЕКТЕП OnLine ХИМИЯ

encontro

on-line

matemática

aplicada

computação

00:00:22

eh

00:00:44

Hello everyone, good

00:00:47

evening, are you listening to me well?

00:01:04

So let's go guys, I think we're going,

00:01:06

okay, so you're

00:01:09

listening

00:01:10

perfectly well, guys, let's go,

00:01:14

let's start our little lesson

00:01:17

today, I'm going to introduce myself first, right?

00:01:20

I'm the tutor teacher,

00:01:23

Carline, I'm with you other

00:01:26

teachers with kess with Felipe with

00:01:30

liis, right? Then I present other

00:01:32

teachers also together in the subject

00:01:35

of mathematics applied to computing,

00:01:37

so, right, we have some topics to

00:01:40

cover here in our subject and

00:01:43

today is an interesting moment, right, for

00:01:46

everyone, let's address some

00:01:49

questions, let's also see some questions

00:01:52

about the work, right, in fact about the

00:01:54

organization of the groups, I think it's

00:01:56

important that we capitulate this part

00:01:58

because we're already approaching the

00:02:01

final period, right, to

00:02:04

register for the teams, it's important that

00:02:06

we recapitulate these aspects too,

00:02:09

then we will revisit some

00:02:12

content from week 4 and week 5,

00:02:14

let me give you my little material

00:02:16

here to guide you, right? Then I will

00:02:20

show you some exercises that

00:02:23

are covered in the book and I also brought

00:02:25

another one that can help you with

00:02:28

understanding of some aspects So

00:02:30

as you know we have a

00:02:32

limited time here on our

00:02:34

broadcast so obviously we

00:02:36

can't cover all the topics that

00:02:38

are covered in two weeks because it's a lot

00:02:40

but I brought up some that I think

00:02:43

are important and are very common or

00:02:46

bring some other applications of them

00:02:48

too and at the end, right, I have one more

00:02:53

invitation to make to

00:02:55

you, so let's go guys, right, just

00:02:57

recapping our agenda, I will

00:02:59

now quickly explain the dynamics of the

00:03:01

groups and we will talk about the contents

00:03:05

of week 4 and week

00:03:08

5, beauty in relation to the dynamics of the

00:03:12

groups. Guys, you can take the time to

00:03:14

register within the

00:03:17

discipline of each of you. It's

00:03:20

important that you are aware

00:03:23

of the information that there can only be four

00:03:24

members per team, but what what's

00:03:27

more, the system won't even

00:03:29

let you

00:03:30

access it, so it's important that you, you know,

00:03:32

have a

00:03:35

maximum of four in the group contract.

00:03:47

any change of team,

00:03:51

right, or group that you think is

00:03:54

necessary And these changes must be

00:03:57

communicated, right, and made by the week,

00:04:00

it's also important that you, right, If

00:04:03

there is any situation, report it to the

00:04:05

Professor or tutor about

00:04:07

any lack of participation by the

00:04:09

members. If there is someone who is

00:04:11

there in the group but did not have any

00:04:12

contribution, then it is important that there is

00:04:16

this communication so that we can

00:04:18

also intervene in some

00:04:20

way. Remembering that everyone must be

00:04:24

registered in a

00:04:27

team only in this way that will open

00:04:30

the option for you to do the delivery of the

00:04:32

activity So if you did the

00:04:35

activity, you were not included

00:04:38

in any team, you will not be

00:04:40

able to deliver the activity

00:04:42

you carried out So this information is very

00:04:44

important, right?

00:04:49

between

00:04:52

you and remembering that you

00:04:55

will only be able to form teams with people from

00:04:58

your class, so be aware of

00:05:00

the class number, if it is final

00:05:03

1 2 3 4, right, be attentive to this

00:05:06

information and you can only form a team

00:05:08

with these people from your class

00:05:11

also remembering that each team

00:05:15

will only deliver one piece of work, so there

00:05:17

is no possibility of you doing

00:05:18

two jobs and delivering them separately,

00:05:21

the team is assembled, four members were there,

00:05:25

something happened,

00:05:27

someone did it separately, you can't, right, you're just going to

00:05:29

do the job. delivery of work there

00:05:32

by

00:05:33

team, beauty guys, so these are the

00:05:37

warnings in relation to

00:05:40

group dynamics, so it is important that you

00:05:42

are aware of this information,

00:05:44

I believe that at some point you must

00:05:47

have already done teamwork, so

00:05:50

these rules are always very

00:05:52

important, right, talk to

00:05:55

your colleagues, maintain a good coexistence or

00:05:58

do work in a personal group, he's

00:06:00

there for a reason, so you exchange

00:06:02

ideas with your colleagues, right, as we

00:06:04

always say, four heads are better

00:06:07

than one, right, so it's important that

00:06:09

you have this communication with your

00:06:11

colleagues because you will be able to

00:06:13

exchange ideas. While you have

00:06:14

any doubts and you can ask

00:06:17

your teammate, look, I

00:06:19

had this doubt here so maybe

00:06:21

that's what I'm thinking, right? So it's

00:06:23

interesting. this exchange that you have and

00:06:26

As I said, there is group work, it

00:06:28

exists for some reason,

00:06:30

but that was it guys, so let's go,

00:06:34

let's go to our

00:06:38

subject content Guys, you must have seen

00:06:40

that we have a lot of content in

00:06:43

weeks 4 and 5. several aspects that are

00:06:46

covered, I will start with you talking

00:06:49

about

00:06:51

dispersion measures, how you should intervene, right?

00:07:07

mean value or the median so when

00:07:11

can we use these measures

00:07:13

of dispersion first we can

00:07:15

use this to evaluate

00:07:16

data constancy So as deviation the

00:07:20

standard can help determine

00:07:22

how close or far the

00:07:25

data points are from the average if the data has a

00:07:28

small dispersion this

00:07:30

suggests that the values are grouped

00:07:32

around the Average on the other hand if

00:07:35

we have a greater dispersion of these

00:07:37

data it indicates a wider distribution

00:07:41

So through these

00:07:43

dispersion measures we can also achieve

00:07:45

this purpose which is to evaluate the

00:07:47

consistency of the data, to understand how

00:07:50

that data that we are

00:07:51

observing, right, that is very important,

00:07:54

in the profession that you are going to pursue, you want to

00:07:56

then do some research and move on to

00:07:58

something like the master's degree, doctorate,

00:08:01

you will also probably work with

00:08:02

this, so you are some of the ways in addition

00:08:06

to what you have already seen in the book

00:08:08

that can be

00:08:10

used can also be used to

00:08:13

compare data sets, so

00:08:16

when we compare two sets of

00:08:19

data the dispersion measures can

00:08:22

indicate how similar or different in

00:08:24

terms of variation This

00:08:26

information is So let's think about an

00:08:29

example comparing the income of two

00:08:33

different regions, thinking here in the region

00:08:36

closest to the coast or

00:08:38

comparing the income of people who live there

00:08:40

closest to the Border So how do

00:08:42

we understand this income of both

00:08:45

regions The standard deviation can help

00:08:48

us in this process, it can determine in

00:08:51

which region the income is more consistent

00:08:53

or the income varies But we can also

00:08:57

use this personally for

00:08:59

risk and investment analysis, perhaps there is an

00:09:02

enthusiast in the financial market area, right?

00:09:04

dealing with this

00:09:07

part, so it is a very

00:09:09

important tool, the measure of dispersion,

00:09:11

especially the standard deviation, they are

00:09:13

crucial to assess the risk associated

00:09:15

with the investment, so an

00:09:18

investment that you are analyzing

00:09:20

that has a high dispersion may be more

00:09:23

risky may have a greater chance of

00:09:25

return variation So there are some

00:09:29

features in addition to what you

00:09:30

saw in the discipline that

00:09:32

dispersion measures can help you so see

00:09:35

how interesting this

00:09:38

part is let's see here some examples

00:09:41

some are there in your book so

00:09:44

let's recap and there are others there that I

00:09:46

brought new ones first, so let's think

00:09:50

about a situation where we have three

00:09:52

students, student a b or c, right, Maria,

00:09:56

José and Joãozinho who took a

00:09:58

test

00:10:00

And these Here were the grades they

00:10:02

got, student a got the grade 7 student B

00:10:05

also had a grade s the student had

00:10:08

a grade of seven if we take the

00:10:12

arithmetic mean of the three students it will be equal

00:10:15

to s so the grades of these students are not dispersed

00:10:19

so they are

00:10:20

concentrated here in the average 7 so they

00:10:25

are not dispersed they coincide with the so-called

00:10:29

ethical arithmetic mean, now let's also think about the

00:10:32

situation here, you know, of the other three students,

00:10:36

student e d and f who take this

00:10:39

same test, so now they are students

00:10:42

from the same class who took the same

00:10:43

test, but now we are looking at these

00:10:45

other separate grades, student D had

00:10:48

a grade of 3.5 the student and 7.5 and student F

00:10:52

had a grade of 10 what is the

00:10:55

arithmetic mean of the students so it will also

00:10:58

be equal to seven But if we look at it in the

00:11:02

same way we will

00:11:05

look at the What is it that the average of these students

00:11:07

are dispersed in relation to the average,

00:11:09

only one student got the maximum grade

00:11:12

close to the Average, sorry, it is equal to

00:11:15

seven, the other students were

00:11:18

dispersed in relation to the average of that

00:11:21

subject, why? Because the average is seven,

00:11:24

then if we observe student D, he

00:11:26

has an average lower than seven and

00:11:29

student F has a grade higher than seven,

00:11:32

who received the maximum grade, so we are

00:11:34

dispersed in relation to that average

00:11:37

S, so by analyzing these two situations

00:11:41

we will understand what the first

00:11:43

situation has a much more

00:11:44

representative result because Because it is more

00:11:46

homogeneous we can understand that

00:11:49

the majority of those scores or in the case of

00:11:52

that specific case all the scores

00:11:55

are seven when compared to the second

00:11:58

situation

00:11:59

in which the average is heterogeneous in

00:12:03

other words, it is a little more dispersed in

00:12:05

relation to the average, they are not as

00:12:07

concentrated close to the average S that was

00:12:11

calculated

00:12:16

previously. The

00:12:18

average deviation, which is the average deviation, right,

00:12:21

folks, is the sum of all the differences

00:12:23

in the absolute value of each one of the

00:12:28

values and the arithmetic mean of all the

00:12:31

elements analyzed so now we are going to

00:12:35

take the mean a math test of

00:12:38

seven students here with

00:12:40

random grades So if we are going to take the

00:12:43

calculation of the arithmetic mean we are going to

00:12:45

add up all the values from that

00:12:47

set of data and let's divide by the

00:12:51

amount of data we have

00:12:53

so let's add them all up 2 + 3 + 5 +

00:12:56

6 + 7 + 9 + 10 and let's divide everything by

00:13:01

S by seven because we have scores from

00:13:04

seven students so the sum of all the

00:13:08

grades will be 40 42 and we will

00:13:11

divide by seven so the average will be

00:13:15

in this case average

00:13:17

six so now we are going to calculate

00:13:20

the difference between each grade and the

00:13:22

average grade which in this case is equal to if and we

00:13:26

will then for the next step So

00:13:29

we take what the grade of each of the

00:13:32

students is and we reduce the value of the Average that

00:13:35

we found

00:13:36

previously so the grade of the student

00:13:38

who got two we take the grade

00:13:41

minus the average It will result in -4 for the

00:13:43

other student -3 -1 0 1 3 4 right, these are

00:13:49

all those grade values minus the

00:13:51

average that we found

00:13:53

previously. So let's transform

00:13:55

all these numbers into positive numbers

00:13:59

and now we will add up all

00:14:02

these numbers and the average deviation is the

00:14:05

average arithmetic of the modules of the seven

00:14:08

calculated values so again

00:14:11

we calculate all the results of this of the

00:14:14

Average minus the grade and we will arrive at the

00:14:18

value of 16 we continue to have the same

00:14:21

seven amounts of information So

00:14:24

let's divide by seven then the

00:14:27

average deviation of these students considering this

00:14:30

grade It's going to be

00:14:36

2.28, so the deviation to get to the

00:14:38

personal standard deviation, we're going to follow a

00:14:40

few more steps, right, the

00:14:44

standard deviation, as I told you, right, it's

00:14:46

used in several different situations,

00:14:49

we can look at investments, we can

00:14:51

look at data consistency, we can

00:14:53

look at risk So there are several things that

00:14:55

we will look at and this personal standard deviation

00:14:58

is defined as the

00:14:59

absolute value of the square root of the variance

00:15:02

so that we can get to the root of the variance,

00:15:05

right, we have some steps to

00:15:07

follow So let's take the example here

00:15:11

of calculate the pattern of the values

00:15:13

found in the table when calculating the

00:15:16

variance in the previous example, so

00:15:19

in the previous example we calculated

00:15:21

the arithmetic mean of the grades, we found

00:15:25

the variance, right, it was the

00:15:27

arithmetic mean of the mean

00:15:29

squared deviations. So we took all those

00:15:32

average deviation values in addition to the

00:15:36

arithmetic mean and we square it and with

00:15:39

that we arrive at the standard deviation which will

00:15:41

be the square root of the variance which is

00:15:48

7.42 let's see another personal example

00:15:52

now thinking about the standard deviation that

00:15:54

we want to calculate In relation to the

00:15:56

music album, I think everyone here

00:15:58

has heard music, they like listening to music.

00:16:01

So I think it will be a very

00:16:03

interesting example, let's think here we

00:16:06

have a music album where each

00:16:09

song has its own running time and

00:16:12

In this album we have five songs,

00:16:15

how are we going to do this? First

00:16:17

we have to convert all of this to the

00:16:19

same format, that is, we are going to transform

00:16:21

this, all these minutes into

00:16:24

seconds, so all the seconds there that

00:16:28

we

00:16:29

have Adding them all up, right the first one

00:16:32

music there on machine part two guys

00:16:34

have the two calculate the

00:16:36

average if we transform the song one that

00:16:39

is 3 minutes and 45 seconds it will have

00:16:42

what 225 seconds the Second song

00:16:46

250 seconds the third song 200

00:16:49

seconds the fourth song 300 seconds and

00:16:53

the fifth song

00:16:55

235

00:16:56

seconds So if we add up all

00:17:00

these results of seconds for each of the

00:17:03

songs on the music album and

00:17:06

divide by five because we have

00:17:08

five five songs on the album we will

00:17:11

have an average of 242 seconds more or

00:17:15

less right there so the average of seconds

00:17:18

of all the songs on the album are

00:17:21

242

00:17:26

seconds so now we are going to

00:17:28

calculate the personal variance we

00:17:31

found the average remember in the first

00:17:34

calculation we found the average which was the

00:17:36

average 242 seconds So we are going to take

00:17:40

those seconds from each of the albums

00:17:44

that we had 3 minutes and a few

00:17:46

seconds and we transformed everything to

00:17:48

second we will take this value minus

00:17:50

the average the result we will

00:17:53

square and this we will do for

00:17:55

each of the songs on the album So the

00:17:59

first song had 225 seconds and the

00:18:03

average of the songs on the album was 242

00:18:06

seconds and this the result of this is

00:18:08

squared so having all

00:18:11

these steps here we will arrive at the

00:18:15

total value, right the total sum of all

00:18:18

these values so 289 + 64

00:18:24

1764 + 3364 + 49 so we have a

00:18:30

total of

00:18:33

5530 so considering that the

00:18:36

standard deviation is the square root of the variance in

00:18:40

absolute values we will divide the sum of the

00:18:45

squared Differences by the number of

00:18:48

songs here there is no mention1 okay guys

00:18:50

to get the variance So we will

00:18:53

take

00:18:55

5530 let's divide by 5 we will have

00:18:59

1106 seconds and it is from this value that

00:19:03

we will do the square root so the

00:19:05

square root of 1106 seconds will be

00:19:11

33.25 seconds So this here is the

00:19:14

personal standard deviation in that album of

00:19:17

five songs that we You can imagine

00:19:20

that it's a rock album, a

00:19:22

samba MPB album, right, the musical style that

00:19:27

you like the most.

00:19:30

We also have asymmetry measures there,

00:19:33

so how are we going to

00:19:36

use these asymmetry measures

00:19:39

in addition to what you're seeing? There in

00:19:42

your course material is a measure of

00:19:46

positive asymmetry, it will tell us

00:19:49

that we have a longer tail to the

00:19:51

right of the graph, which means that

00:19:54

there are higher values than the

00:19:57

average, we will see

00:20:00

these graphs a little better one

00:20:02

negative asymmetry measure will indicate a

00:20:05

longer tail to the left of the graph

00:20:08

with values smaller than the average

00:20:12

of those values there that we from that

00:20:14

set of data that we are observing,

00:20:17

how can we use this in

00:20:19

financial analysis also because if

00:20:22

we look Using a set of data,

00:20:24

we put this in a

00:20:27

graphical representation, we will be able to understand the

00:20:30

distribution of returns on

00:20:32

investments, investments that will have

00:20:35

a positive symmetry are more

00:20:38

likely to have higher returns

00:20:40

since they have

00:20:42

higher values than the average there on the right part

00:20:46

of the

00:20:48

graph but they can also have

00:20:51

long tails with extreme losses and a

00:20:54

negative symmetry indicates that returns

00:20:57

can have a distribution with more

00:20:59

losses than gains. So if we

00:21:02

put all this information there on

00:21:04

that graph we can have a

00:21:07

visualization little better on

00:21:11

financial aspects and here folks we have

00:21:13

the graphical representation of each

00:21:16

of these elements that I mentioned, a

00:21:18

symmetrical distribution will be one in

00:21:21

which the median, the mean and the mode are all the

00:21:26

same if we look at a

00:21:30

symmetrical distribution to the right or positive As I

00:21:33

mentioned to you, we will have the mean

00:21:37

greater than the median and greater than the

00:21:41

mode. If we look at an

00:21:45

asymmetric distribution to the left, we

00:21:49

will have an average lower than the

00:21:52

median and lower than the mode. So this

00:21:56

set of graphs Here it's just

00:21:58

important for us to understand various

00:22:00

aspects, whether it's your course, your

00:22:04

day-to-day life, or something

00:22:06

you want to do beyond college

00:22:09

that will give you several possibilities for

00:22:16

personal interpretation.

00:22:34

Regarding

00:22:37

personal kurtosis, we also have this

00:22:40

graphical representation, right? We have

00:22:42

tails of a distribution that are

00:22:44

different from the normal distribution.

00:22:57

Look at a

00:23:01

graph, right, what will represent

00:23:04

that set of data that

00:23:06

we are analyzing, we can

00:23:09

understand more or less how

00:23:11

that

00:23:13

data behaves, so kurtosis

00:23:16

aims to facilitate the understanding of the

00:23:18

general characteristics of the distribution of

00:23:21

the data under

00:23:22

analysis indicates the degree of flattening or

00:23:25

tapering of a frequency distribution

00:23:29

or Considering the stereogram of the

00:23:31

question in question, sorry, when

00:23:35

we have a kurtosis equal to zero, we

00:23:37

will have the data uniformly

00:23:41

distributed, so there is a very

00:23:44

nice curve there that goes up and down having

00:23:49

the same parameters, which is different from

00:23:52

what we observe here in a

00:23:55

positive kurtosis that occurs, you know, a

00:23:59

heavier tail distribution as you are

00:24:01

seeing here in relation to a

00:24:03

normal distribution, so we see that this is the

00:24:07

solid blue line that the We

00:24:09

previously observed that this

00:24:11

normal distribution represents the positive kurtosis,

00:24:16

it is dotted, right? So it will indicate a

00:24:19

positive value for the short ones.

00:24:28

are

00:24:33

higher and the negative kurtosis we will

00:24:36

have a different graph than the other

00:24:40

two obviously, right class

00:24:43

negative kurtosis it will occur in a

00:24:45

distribution with lighter tails in

00:24:48

relation to a normal distribution So

00:24:51

this dotted line again we

00:24:54

will have it more concave, right, we're going to have

00:24:58

it with a

00:25:01

very uniform distribution too, but with

00:25:04

negative values for kurtosis and it has a

00:25:07

representation that's very different from

00:25:11

kurtosis there, equal to

00:25:15

zero, so when are we going

00:25:17

to use kurtosis, right guys, again

00:25:20

bringing examples in addition to what you

00:25:22

're seeing In your material,

00:25:25

risk and insurance analysis, we

00:25:28

will use it to understand the

00:25:30

probability of extreme events.

00:25:33

When we are looking at some risk or

00:25:35

even some insurance, a high kurtosis

00:25:39

can indicate a greater risk for

00:25:41

unexpected or extreme events in a

00:25:45

portfolio of investment or in an

00:25:47

insurance context So it can

00:25:49

indicate that there may be more

00:25:52

extreme events there, precisely, right? Because of

00:25:54

that little tip that we have up there,

00:25:56

this can also be applied in

00:25:59

biology and medicine studies, just getting away

00:26:02

from what we see in the

00:26:03

discipline, then out of nowhere,

00:26:05

examples of biology and medicine appeared here,

00:26:07

so when we are talking about

00:26:09

medical biological studies, it can help to

00:26:12

understand the biological

00:26:15

or biometric characteristics and Studies, right, of height,

00:26:19

weight or blood pressure, can this

00:26:22

kurtosis indicate if there is a

00:26:24

common concentration of values close to the average, right,

00:26:27

so let's

00:26:28

analyze a set of

00:26:31

information again and understand whether that

00:26:34

blood pressure or weight or height, right, will have

00:26:37

some common concentration in some type

00:26:39

of age population or in that

00:26:42

little piece of data that is being

00:26:46

shown. and last but not

00:26:49

least we have the

00:26:51

arithmetic mean. So the averages, the grades, you know,

00:26:55

in mathematics again, going back to

00:26:58

our great example

00:27:00

of grades in

00:27:02

mathematics, we can calculate the

00:27:05

arithmetic mean of the grades from two

00:27:08

partial assessments, right, so we

00:27:10

can calculate the value of the grades P1 and

00:27:14

P2 and we can divide

00:27:17

these

00:27:18

values by two. So if we use

00:27:21

two values, what will we do?

00:27:23

Take those two values, add them and divide

00:27:25

by two. This will be the arithmetic mean

00:27:29

represented here in this formula,

00:27:31

right, which is o o a plus b divided by 2

00:27:36

if we use an arithmetic mean

00:27:38

of three numbers we will add the

00:27:40

values of a b c divide by three because

00:27:44

there are three different values that we

00:27:46

are looking at but we can also

00:27:49

look personally this for an

00:27:52

arithmetic mean of n numbers of several

00:27:54

numbers So we will have what the

00:27:57

formula a b + c plus z divided by

00:28:03

by n which will then give us the formula

00:28:05

to understand the arithmetic mean in a

00:28:08

large set of data Of course we

00:28:11

have Excel's help, right, Excel helps

00:28:14

everyone a lot, but it's important that

00:28:16

we know what's behind Excel, right? So how is

00:28:18

Excel calculating

00:28:20

that arithmetic mean? How is Excel

00:28:23

calculating the standard deviation, right? So it's

00:28:25

important that We know this, but

00:28:27

Excel is there, so we know that it

00:28:29

also helps us with these

00:28:33

calculations. And we also have the famous

00:28:37

arithmetic mean, weighted or just the

00:28:41

weighted mean, so for us to

00:28:44

calculate the weighted mean, it's a

00:28:47

little different. The idea is the same

00:28:49

but we have some different steps

00:28:51

to follow So let's think about

00:28:54

calculating the

00:28:55

arithmetic mean of this quantity here

00:28:59

of

00:29:00

elements that are being shown here

00:29:03

in the second line we have some

00:29:05

quantities of 3 4 5 6 and 7 So how

00:29:10

do we do this, right? We are going to count

00:29:14

the number of times each number

00:29:17

appears, three will appear four times

00:29:20

in this data set, four appears

00:29:22

twice, five appears once, six

00:29:25

appears five, seven appears three times,

00:29:28

so the weighted average or

00:29:31

arithmetic mean weighted is obtained

00:29:33

more quickly when we calculate the

00:29:35

sum of the products of each number

00:29:39

multiplied by the number of times

00:29:41

it is repeated then divided by the total number

00:29:44

of numbers

00:29:47

considered. So we will take these

00:29:50

values

00:29:51

and arrive at the result of 76

00:29:57

divided

00:30:00

by 15 which will be

00:30:04

5.06

00:30:06

guys so that being said,

00:30:08

these here guys are the contents

00:30:12

that we managed to bring to

00:30:15

you today, right, they are just some, as

00:30:18

you may have seen that we have

00:30:22

several in our material,

00:30:25

but but we We can't address

00:30:28

everyone, right, we have limited time and we

00:30:31

will now have an

00:30:35

interactive connection and I left the invitation here

00:30:38

for you guys, I left the

00:30:41

QR code here for each of you if

00:30:44

you want to watch and I'll share the

00:30:47

access link Also here in the

00:30:51

chat let me get the link

00:30:59

and here is the personal link so

00:31:02

you can watch the

00:31:05

interactive connection that today will be about

00:31:08

Maker culture and

00:31:11

[Music]

00:31:12

society Oh I see there are some questions

00:31:16

about the test, right the teacher kess

00:31:18

is helping us,

00:31:29

beauty guys, I sent it here in the chat and

00:31:31

I will send it here again the link to the

00:31:33

interactive connection that we will already

00:31:37

have, which is Maker culture and society,

00:31:41

Professor Kess answered the questions about

00:31:46

the test, right?

00:31:49

to answer

00:32:12

just a second,

00:32:31

guys, in the world, the teacher k is helping

00:32:34

you answer the questions, right about

00:32:37

the

00:32:38

test, so I would like to thank

00:32:40

all of you for participating and my name,

00:32:44

right, is the tutor teacher Cassiana, the

00:32:46

tutor teacher Felipe, the

00:32:48

tutor teacher kess who is helping us here,

00:32:50

thank you very much, kess, tutor

00:32:53

Lis and Luana,

00:33:01

I will also go back to the previous slide

00:33:04

so that you

00:33:06

can project here if you want to access it on your

00:33:09

cell phone QR Code You don't have the

00:33:12

interactive connection link here in the chat, which

00:33:15

you can also

00:33:17

access The recording will be made available

00:33:20

to you and soon it will also be

00:33:23

available in the week's materials.

00:33:31

This has already started, right, it's already

00:33:35

19:34,

00:33:36

the Live, right,

00:33:47

interactive connection, beauty guys, so good

00:33:50

evening

00:33:51

everyone, thank you very much for

00:33:54

your presence, if you have any questions, right, you can go there

00:33:58

Don't talk to us, I'll be

00:33:59

answering this for you Oh, Professor

00:34:03

Kess shared here that everyone

00:34:05

will receive instructions in the notices, right,

00:34:10

instructions will be made available closer to the test.

00:34:13

So it will help you a lot to clear up

00:34:16

any doubts that may still

00:34:20

arise Good evening

00:34:24

Guys, thank you very much,

00:34:26

Antônio, being with us

00:34:29

today, have a good weekend for you, have a

00:34:32

good weekend,

00:34:39

everyone, this is on the 15th, can you talk?

00:34:45

Thank you, have a good night, have a good

00:34:47

weekend, too, may

00:34:49

God bless you, can you continue? I'm here, you can

00:34:52

continue talking, I'm here Great,

00:34:54

thank you

00:34:55

Antônio, we also have the

00:34:58

interactive connection, you will see the module evaluations,

00:35:00

which you will soon

00:35:03

receive, right? Some information, the

00:35:19

most perfect, Professor Kess asked

00:35:22

to reinforce and we will reinforce again on the

00:35:25

15th/ 04 we will have the interactive connection

00:35:30

to the summer module evaluations

00:35:33

so you will receive this link,

00:35:39

the information with the time access link will be available in various ways

00:35:41

so it is important that you

00:35:44

watch this interactive connection because

00:35:46

I'm sure that In addition to the notices that

00:35:48

we will send to you in the

00:35:51

disciplines, it is still important that you are

00:35:53

part of the interactive connection to resolve

00:35:55

other doubts that may

00:35:57

arise and everything else with all

00:36:12

possible guidance, that's right, all doubts

00:36:15

are answered regarding

00:36:24

personal assessments. A big hug to everyone, thank you very much

00:36:28

and see you guys

Description:

TIPO Profa.-Tutora: Disciplina: Tema: Data: 00/00/0000

Preparing download options

Popular

HD video

Only sound

All

* — If the video is playing in a new tab, go to it, then right-click on the video and select "Save video as..."

** — Link intended for online playback in specialized players

Questions about downloading video

How can I download "Encontro On-line Matemática aplicada a computação" video?

http://unidownloader.com/ website is the best way to download a video or a separate audio track if you want to do without installing programs and extensions.
The UDL Helper extension is a convenient button that is seamlessly integrated into YouTube, Instagram and OK.ru sites for fast content download.
UDL Client program (for Windows) is the most powerful solution that supports more than 900 websites, social networks and video hosting sites, as well as any video quality that is available in the source.
UDL Lite is a really convenient way to access a website from your mobile device. With its help, you can easily download videos directly to your smartphone.

Which format of "Encontro On-line Matemática aplicada a computação" video should I choose?

The best quality formats are FullHD (1080p), 2K (1440p), 4K (2160p) and 8K (4320p). The higher the resolution of your screen, the higher the video quality should be. However, there are other factors to consider: download speed, amount of free space, and device performance during playback.

Why does my computer freeze when loading a "Encontro On-line Matemática aplicada a computação" video?

The browser/computer should not freeze completely! If this happens, please report it with a link to the video. Sometimes videos cannot be downloaded directly in a suitable format, so we have added the ability to convert the file to the desired format. In some cases, this process may actively use computer resources.

How can I download "Encontro On-line Matemática aplicada a computação" video to my phone?

You can download a video to your smartphone using the website or the PWA application UDL Lite. It is also possible to send a download link via QR code using the UDL Helper extension.

How can I download an audio track (music) to MP3 "Encontro On-line Matemática aplicada a computação"?

The most convenient way is to use the UDL Client program, which supports converting video to MP3 format. In some cases, MP3 can also be downloaded through the UDL Helper extension.

How can I save a frame from a video "Encontro On-line Matemática aplicada a computação"?

This feature is available in the UDL Helper extension. Make sure that "Show the video snapshot button" is checked in the settings. A camera icon should appear in the lower right corner of the player to the left of the "Settings" icon. When you click on it, the current frame from the video will be saved to your computer in JPEG format.

What's the price of all this stuff?

It costs nothing. Our services are absolutely free for all users. There are no PRO subscriptions, no restrictions on the number or maximum length of downloaded videos.