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Inferential Statistics

The Z‑Score

The Z‑score, togeth­er with the Empir­i­cal Rule, pro­vides a frame­work for under­stand­ing the vari­abil­i­ty of data, stan­dard­iz­ing mea­sure­ments, and mak­ing informed deci­sions about hypothe­ses in the con­text of inher­ent ran­dom vari­a­tion in data. Learn how to apply the Empir­i­cal Rule to nor­mal­ly dis­trib­uted data, how to cal­cu­late Z‑scores, and how these are used in hypoth­e­sis testing.

Course Videos

1

Inferential Statistics Overview and Sampling

05:25

 2

Hypothesis Testing Overview

07:49Members only

 3

Normality

07:36Members only

 4

Central Limit Theorem

07:25Members only

Current Video

The Z-Score

07:40Members only

6

Next VideoCreating Run Charts

04:56Members only

 7

1-Sample t-Test

06:34Members only

 8

2 Variances Test

08:58Members only

 9

2 Sample t-Test

07:53Members only

 10

Paired t-Test

06:30Members only

 11

1-Proportion Test

04:49Members only

 12

2 Proportions Test

06:46Members only

 13

Chi Square Test

11:09Members only

 14

1 Sample Sign Test

03:59Members only

 15

Mann-Whitney Test

04:48Members only

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Next Video Creating Run Charts 

It’s impor­tant for our data to be the result of ran­dom sam­pling because if not, it will lead to inac­cu­rate results and con­clu­sions. The Run Chart allows us to test our data set for ran­dom­ness and inde­pen­dence before con­duct­ing more advanced sta­tis­ti­cal analy­sis. Learn the com­po­nents of a run chart and the tests we can run to check for ran­dom­ness of our data.