This summary contains:
- Complete summary of all lectures and practice units of Statistics for premaster for Communication and Information sciences.
- STEP BY STEP GUIDE ON HOW TO PERFORM ANALYSIS IN JAMOVI.
- Practice units
- Notes
Subjects:
1 Introduction
2 Distribution
3 Sample to popu...
From average error to SD ............................................................................................................................ 9
Types of graphs ......................................................................................................................................... 10
Lecture 3 Sample to population .................................................................................................................... 14
Normal distribution ................................................................................................................................... 14
INGREDIENT 3 – T HE S TANDARD E RROR ................................................................................................... 16
The P-value ............................................................................................................................................ 20
Lecture 5 Compare means ............................................................................................................................ 25
Knowing which tests to choose ................................................................................................................. 33
Process of T-testing in picture ................................................................................................................... 34
Lecture 6 Anova and Reliability analysis ....................................................................................................... 36
Omnibus ANOVA ....................................................................................................................................... 38
ANOVA by hand ..................................................................................................................................... 41
Effect sizes and ANOVA ............................................................................................................................. 42
Three types of correlation: ........................................................................................................................ 71
Lecture 11 Linear regression part 1............................................................................................................... 79
Relations between 2 continuous variables = linear regression ................................................................. 79
Regression vs correlation .......................................................................................................................... 87
Lecture 12 Linear regression part 2............................................................................................................... 89
JAMOVI STEP BY STEP LECTURE 1 TO 6 ....................................................................................................... 131
Using Filters in Jamovi ............................................................................................................................. 131
One-sample T-test in Jamovi ................................................................................................................... 131
Report one-sample t-test set up ......................................................................................................... 132
Independent sample t-test in Jamovi ...................................................................................................... 134
Report independent sample t-test set up ........................................................................................... 136
Report paired sample t-tests set up .................................................................................................... 140
Reliability Analysis in Jamovi ................................................................................................................... 142
ANOVA - Omnibus ................................................................................................................................... 144
, LECTURE 1 INTRODUCTION
The principles of hypothesis testing “Women are more intelligent than men”
• N=2, men score 108 and women score 109
• Is my hypothesis supported or not? What if N=10, 100? 100?
Point of departure -> assumption that there is no difference.
• This gives a point of comparison
• If no difference, than IQ(women) – IQ(men) = 0
• We can predetermine: if I measure in 1000 persons, and the mean difference between men and
women is larger than 5IQ-points, then it is very unlikely that this difference is coincidence.
Types of hypothesis
Null hypothesis, H0 this is the one we try to reject
• There is no effect expected (most of the time)
• This is generally the outcome
• For example: “woman are equally likely as men to wear a skirt or dress” or “there is no
relationship between age and the number of wrinkles you have”
The alternative hypothesis, H1 Woman are more likely to wear a skirt or dress than man
• If we can reject H0, this one is supported by the data but not proven.
• “There is a positive relationship between age and the number of wrinkels you have.”
In Statistics, we try to reject the null hypothesis. If we can reject H0, this one is supported by the data
but not proven. Shoe size example. if we have a class of 100 people and the average size is 40. We try
to predict the future. Only two people have a size 46. How likely is it that the first person who comes
in has as size 46?
Statistics offer u a means to determine exactly how (un-)likely it is that we would observe a set of data if
the null hypothesis is true. In other words, we examine the chance the null hypothesis is true. If it is very
unlikely (smaller than 5%) we may conclude that the alternative hypothesis is not true.
Experiment
- You manipulate something
- This is supposed to have an effect
- In other words: cause -> effect.
- The manipulated variable is the independent variable.
- The effect is the dependent variable.
I want to study the effect of colour clothing on how hot you feel. -> you can manipulate this.
➔ Independent is the colour of the shirt
➔ Dependent is how hot you feel.
Correlational design
You measure/observed perceived reality. For example: Do people get more wrinkles as they grow older?
-> you cannot manipulate this.
1. Examine association
Is depression associated with poor health?
2. Predictor -> outcome variable
Does lecture attendance predict grade?
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