Error)

In general,

there are two types of errors we can make while testing:

Type I error (False

positive) and Type II Error (False negative).

Statisticians summarize

the errors in the following

table:

The probability of committing Type I error

(False positive) is equal to the significance level (α).

The probability of committing Type II error (False

negative) is equal

to the beta (β). If you want to find out more about statistical errors, just follow this link for an article written by your

instructor.

1- Types of data and level of measurement
2- Graphs and Tables that Represent Categorical Variables
3- Excel formulas
4- Graphs and tables that represent numerical variables
5- Graphs and Tables for Relationships Between Variables.
6- Mean, Median, Mode
7- Variance and Standard Deviation
8- Covariance and Correlation
9- Distributions
10- The Central Limit Theorem
11- Estimators and Estimates
12- Confidence Intervals and the Margin of Error
13- Student’s T Distribution
14- Formulas for Confidence Intervals
15- Scientific method
16- Hypotheses
17- Decisions You Can Take
18- Statistical Errors (Type I Error and Type II Error)
19- P-Value
20- Formulae for Hypothesis Testing
21- Basics
22- Linear regression equation
23- How to do linear regression in Excel with Analysis ToolPak
24- Interpret regression analysis output
25- How to make a linear regression graph in Excel
26- How to do regression in Excel using formulas