Explore
For Enterprise
Join for Free
Log In

deeplearning.ai
AI is everywhere. Now there’s AI for Everyone.
AI is not only for engineers. This non-technical course will help you apply AI in your own organization.
University of North Texas
Easily transfer credits to earn your bachelor’s degree faster
Earn your bachelor’s degree online from UNT, a top 9 research school in Texas.

Explore todo o Coursera

Navegar
Os melhores cursos
For Enterprise
Entrar
Inscreva-se gratuitamente

1.08 Example

Loading...

Estatística básica

Universidade de Amsterdã

4.7 (2,447 classificações) | 160K alunos inscritos

Curso 3 de 5 no Métodos e estatística aplicados às Ciências SociaisMétodos e Estatística Aplicados às Ciências Sociais Programa de cursos integrados

Cadastre-se gratuitamente

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work. The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests. You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.

Visualizar o programa do curso

Habilidades que você aprenderá

Statistics, Confidence Interval, Statistical Hypothesis Testing, R Programming

Avaliações

4.7 (2,447 classificações)

5 stars
1,854 ratings
4 stars
469 ratings
3 stars
77 ratings
2 stars
21 ratings
1 star
26 ratings

DA

Aug 06, 2016

One of the best courses of statistics for the beginners. The concepts are well explained, the learning path well researched and above everything the R labs were ideal for the beginners.

EM

Jan 09, 2016

Only the firs week of this course, but I can already tell that it's going to be incredibly useful to me. I've learned a lot and especially love the introduction to R through datacamp!

Na lição

Exploring Data

In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved.

1.07 Z-scores4:38

1.08 Example6:46

Ministrado por

Matthijs Rooduijn
Dr.
Emiel van Loon
Assistant Professor

Experimente o curso Gratuito

Transcrição

Say I live in a city with eight high schools. I want to know what per high school, the average grade for chemistry is. The lowest possible grade is a zero and the highest possible grade is a 10. This is the data matrix. You can see that the case is studied here are not individual students but schools. The variable of interest is the average grade for chemistry. Now, imagine you want to know a couple of things. First, you want to know what the distribution of the variable average grade for chemistry looks like. Second, you want to know what the center of the distribution is. Third, you want to know more about the variability of the distribution. Fourth, you want to make a box plot to visually represent center variability and outliers. And fifth, you want to know what the z-score of schools three tells you. Let's start with the first question. You want to know what the distribution looks like. We're dealing with a quantitative variable here and a small sample size. So the best way to display the distribution is with a dot plot. The possible grades range from 0 to 10. So you mark these values on the horizontal line of your dot plot. Next, you edit that's for all the observations. The first school got a 7.4, so you place the dot here. You do that for all eight schools. This is the result. You can see that there is one outlier, schools three has an average grade of only 4.1. The second thing you want to know is what the center of the distribution is. Well, you know that we have three measures of central tendency, the mode, the median, and the mean. Let's start with the mode. There is one value that occurs twice, 7.4, so the mode is 7.4. The median is the middle value, when we order of values from low to high. This is the order of the values. We have to middle values, 7.1 and 7.4. The average of these two values is 7.1 plus 7.4 divided by 2 equals 7.25. That's our median. To compute the mean, we use this formula. First, we add up all values, and then we divide that outcome by the size of our sample. That makes 54.9 divided by 8 equals 6.86. This shows you, that a relatively extreme score of schools three here causes the mean to be lower than the median. Thirdly, we want to know how far the values of the distribution are spread out. We know three measures of dispersion. The first one is the range. The range is equal to the largest value minus the smallest value. That is 8.1 minus 4.1 equals 4. The interquartile range is the difference between the first and the third quarter. So, first we have to compute Q1 and Q3. This was how we computed the median. We do the same for the left side of the distribution. Q1 is the average of 6.2 and 6.7, that is 6.45. And for the right side, that's the average of 7.4 and 7.9, that equals 7.65. Q3 minus Q1 equals 7.65 minus 6.45 equals 1.2. Now the third measure of dispersion, the standard deviation. It's a bit more work to compute it. We need this formula. First, we subtract the mean from every individual score. So that's 7.4 minus 6.86 equals 0.54. We do that for all values. This is the result. Next, we square all these values, 0.54 squared equals 0.2916. We do that again for all values and add these scores up. That makes 11.3388. We have now finished this part of the formula. The next step is to divide by n minus 1. That's 8 minus 1 is 7. 11.3388 divided by 7, makes about 1.6. As a final step, we have to take the square root of this outcome. That's about 1.27. That's our standard deviation. The fourth thing we wanted to do with our data was making a box plot. We already have all the information we need. We have Q1 and Q3, that determined the borders of our box. So our box goes from here, 6.45 to here 7.65. We display the median or in other words Q2, with a horizontal line here at 7.25. And do we have outliers? Outliers are values that are more than one and a half IQR below Q1 or above Q3. One and a half IQR equals 1.5 multiplied with 1.2, that equals 1.8. 1.8 below 6.45 means that values below 6.45 minus 1.8 equals 4.65 are outliers. We have one such value, 4.1. We display the outlier with a dot. The end of the lower whisker is the minimum score that is not an outlier. That's 6.2, that's here. 1.8 above 7.65 means that values above 7.65 plus 1.8 equals 9.45 are outliers as well. We don't have values this high, so there are no outliers on this side of the box. The end of the best score is the maximum value which is 8.1, and follow, here's a box plot. The box plot shows at a glance that our observations lie between approximately six and eight, and that we have one clear outlier. The fifth and final thing we want to know is the z-score of school three. This is the formula. So that makes 4.1 minus 6.86 divided by 1.27, that makes minus 2.17. This indicates that this value lies 2.17 standard deviations below the mean and can therefore be conceived of as a rather exceptional value. So, what can we conclude? If your plan was to send your children to school number three, think about it twice. Although that might be a good reason why the grade is so low, you'd at least want to know why.

Explore nosso catálogo

Registre-se gratuitamente e obtenha recomendações, atualizações e ofertas personalizadas.

Coursera

Sobre
Liderança
Carreiras
Lista de cursos
Certificados
Certificados MasterTrack™
Notas
For Enterprise
For Government
For Campus

Comunidade

Aprendizes
Parceiros
Desenvolvedores
Testadores beta
Tradutores
Blog
Tech Blog

Mais

Termos
Privacidade
Ajuda
Acessibilidade
Imprensa
Contato
Diretório
Afiliados

Aprenda em qualquer lugar

Siga-nos

© 2020 Coursera Inc. Todos os direitos reservados.