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sowiso logo Statistics for Sciences

Statistics for college and university students. Contains descriptive statistics, probability theory, inferential statistics, hypothesis testing, data analysis and more. Statistics for Sciences takes a more mathematical approach to explaining and assessing statistics.

Available languages: 
en
Course content
[A, SfS] Chapter 1: Sampling, Descriptive Statistics, Intr
1.1: Populations and Samples
THEORY
T
1.
Populations and Samples
PRACTICE
P
2.
Populations and Samples
11
1.2: Random Sampling
THEORY
T
1.
Random Sampling
PRACTICE
P
2.
Random Sampling
4
1.3: Variables
THEORY
T
1.
Variables
PRACTICE
P
2.
Variables
3
1.4: Measurement Levels
THEORY
T
1.
Measurement Levels
PRACTICE
P
2.
Measurement Levels
12
1.5: Distribution of Quantitative Data
THEORY
T
1.
The Distribution of Quantitative Data
1.6: The Mean
THEORY
T
1.
The Mean
PRACTICE
P
2.
The Mean
8
1.7: The Median
THEORY
T
1.
The Median
PRACTICE
P
2.
The Median
5
1.8: Quartiles
THEORY
T
1.
Quartiles
PRACTICE
P
2.
Quartiles
4
1.9: Frequency Tables
THEORY
T
1.
Frequency Tables
PRACTICE
P
2.
Frequency Tables
2
1.10: Variance and Standard Deviation
THEORY
T
1.
Variance and Standard Deviation
PRACTICE
P
2.
Variance and Standard Deviation
2
[A, SfS] Chapter 2: Probability
2.1: Probability Principles
THEORY
T
1.
Probability Principles
PRACTICE
P
2.
Probability Principles
9
2.2: Counting Methods
THEORY
T
1.
Counting Methods
PRACTICE
P
2.
Counting Methods
9
2.3: Conditional Probability and Independence
THEORY
T
1.
Conditional Probability and Independence
PRACTICE
P
2.
Conditional Probability and Independence
8
2.4: Random Variables
THEORY
T
1.
Random Variables
PRACTICE
P
2.
Random Variables
19
2.5: Linear Combinations of Random Variables
THEORY
T
1.
Linear Combinations of Random Variables
PRACTICE
P
2.
Linear Combinations of Random Variables
10
[A, SfS] Chapter 3: Probability Distributions
3.1: The Binomial Distribution
THEORY
T
1.
The Binomial Distribution
PRACTICE
P
2.
The Binomial Distribution
7
3.2: The Normal Distribution
THEORY
T
1.
The Normal Distribution
PRACTICE
P
2.
The Normal Distribution
14
3.3: Normal Quantile-Quantile Plots
THEORY
T
1.
Normal Quantile-Quantile Plots
PRACTICE
P
2.
Normal Quantile-Quantile Plots
1
3.4: More Probability Distributions
THEORY
T
1.
More Probability Distributions
PRACTICE
P
2.
More Probability Distributions
9
3.5: The Central Limit Theorem
THEORY
T
1.
The Central Limit Theorem
PRACTICE
P
2.
The Central Limit Theorem
5
[A, SfS] Chapter 4: Estimation
4.1: Bias, Variance, and Mean Square
THEORY
T
1.
Bias, Variance and Mean Square Error (MSE)
PRACTICE
P
2.
Bias, Variance, and Mean Square Error (MSE)
7
4.2: Maximum Likelihood Estimation
THEORY
T
1.
Maximum Likelihood Estimation
PRACTICE
P
2.
Maximum Likelihood Estimation
2
[A, SfS] Chapter 5: Confidence Intervals
5.1: Confidence Intervals
THEORY
T
1.
Confidence Intervals
PRACTICE
P
2.
Confidence Intervals
2
5.2: CI for a mean
THEORY
T
1.
Confidence Interval for the Population Mean of a Quantitative Variable
PRACTICE
P
2.
Confidence Interval for the Population Mean of a Quantitative Variable
10
5.3: CI for mean difference
THEORY
T
1.
Confidence Interval for the Population Mean Difference for a Quantitative Variable
PRACTICE
P
2.
Confidence Interval for the Population Mean Difference for a Quantitative Variable
7
5.4: CI for difference in means
THEORY
T
1.
Confidence Interval for the Difference Between Two Population Means for a Quantitative Variable
PRACTICE
P
2.
Confidence Interval for the Difference Between Two Population Means for a Quantitative Variable
9
5.5: CI for proportion
THEORY
T
1.
Confidence Interval for a Population Proportion
PRACTICE
P
2.
Confidence Interval for a Population Proportion
4
5.6: CI for difference in proportions
THEORY
T
1.
Confidence Interval for the Difference Between Two Population Proportions
PRACTICE
P
2.
Confidence Interval for the Difference Between Two Population Proportions
3
[A, SfS] Chapter 6: Hypothesis Testing
6.1: Theory of Hypothesis Testing
THEORY
T
1.
Theory of Hypothesis Testing
PRACTICE
P
2.
Theory of Hypothesis Testing
15
6.2: Test for Population Mean
THEORY
T
1.
Hypothesis Test for a Population Mean
PRACTICE
P
2.
Hypothesis Test for a Population Mean
7
6.3: Test for Population Mean Difference
THEORY
T
1.
Hypothesis Test for a Population Mean Difference
PRACTICE
P
2.
Hypothesis Test for a Population Mean Difference
1
6.4: Test for Difference in Two Population Means
THEORY
T
1.
Hypothesis Test for a Difference in Two Population Means
PRACTICE
P
2.
Hypothesis Test for a Difference in Two Population Means
6
6.5: Test for Population Proportion
THEORY
T
1.
Hypothesis Test for a Population Proportion
PRACTICE
P
2.
Hypothesis Test for a Population Proportion
5
6.6: Test for Difference Between Proportions
THEORY
T
1.
Hypothesis Test for a Difference Between Two Population Proportions
PRACTICE
P
2.
Hypothesis Test for a Difference Between Two Population Proportions
2
6.7: Computing the Power of a Hypothesis Test
THEORY
T
1.
Computing the Power of a Hypothesis Test
PRACTICE
P
2.
Computing the Power of a Hypothesis Test
4
6.8: Tests & Confidence Intervals in R
THEORY
T
1.
Hypothesis Tests and Confidence Intervals in R
PRACTICE
P
2.
Hypothesis Tests and Confidence Intervals in R
16
[A, SfS] Chapter 7: Chi-square tests and ANOVA
7.1: Chi-square Goodness of Fit Test
THEORY
T
1.
Chi-square Goodness of Fit Test
PRACTICE
P
2.
Chi-square Goodness of Fit Test
2
7.2: Chi-square Test of Independence
THEORY
T
1.
Chi-square Test of Independence
PRACTICE
P
2.
Chi-square Test of Independence
5
7.3: One-way Analysis of Variance
THEORY
T
1.
One-way Analysis of Variance
PRACTICE
P
2.
One-way Analysis of Variance
9
7.4: Multiple Pairwise Comparisons
THEORY
T
1.
Multiple Pairwise Comparisons
PRACTICE
P
2.
Multiple Pairwise Comparisons
4
7.5: Two-way Analysis of Variance
THEORY
T
1.
Two-way Analysis of Variance
PRACTICE
P
2.
Two-way Analysis of Variance
10
[A, SfS] Chapter 8: Correlation and Regression
8.1: Correlation
THEORY
T
1.
Correlation
PRACTICE
P
2.
Correlation
9
8.2: Simple Linear Regression
THEORY
T
1.
Simple Linear Regression
PRACTICE
P
2.
Simple Linear Regression
21
8.3: Validating Assumptions and Remediation
THEORY
T
1.
Validating Assumptions and Remediation
PRACTICE
P
2.
Validating Assumptions and Remediation
15
8.4: Multiple Linear Regression
THEORY
T
1.
Multiple Linear Regression
PRACTICE
P
2.
Multiple Linear Regression
10
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