A regional store manager wants to test whether increasing store hours increases profits, so they randomly select half of their locations to stay open an extra hour later in the evenings and compare profits between stores at the end of the month. They notice that stores open later saw higher profits on average. Is this an experiment or an observational study? Can they determine the extra hours caused the increase in sales?
Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically1h 48m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables2h 55m
- 6. Normal Distribution & Continuous Random Variables1h 48m
- 7. Sampling Distributions & Confidence Intervals: Mean2h 8m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 20m
- 9. Hypothesis Testing for One Sample2h 23m
- 10. Hypothesis Testing for Two Samples3h 25m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 30m
- 14. ANOVA1h 4m
1. Introduction to Statistics
Intro to Collecting Data
Struggling with Statistics for Business?
Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
A software development company created a new app for fitness, and they want to determine if using the app can lead to weight loss and increased strength in customers. Should they run an observational study or experiment?
A
Observational study
B
Experiment
C
Survey
D
Case study

1
Understand the goal of the study: The company wants to determine if using the app causes weight loss and increased strength. This implies a cause-and-effect relationship needs to be established.
Recognize the difference between study types: An observational study observes subjects without intervention, while an experiment involves manipulating variables to observe effects. A survey collects self-reported data, and a case study provides an in-depth analysis of a single subject or group.
Identify the need for control: To establish causation, the company must control for other factors (e.g., diet, exercise habits) that could influence weight loss and strength. This is best achieved through an experiment.
Design the experiment: Randomly assign participants to two groups—one using the app and one not using it (control group). Measure weight loss and strength changes over time in both groups.
Analyze the results: Use statistical methods (e.g., t-tests or ANOVA) to compare the outcomes between the app users and the control group, determining if the app has a significant effect.
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