"Rags-to-riches TV programs" are television shows that are based in reality and feature "regular people" competing for prizes with the potential to change lives, such as home remodels, business start-up funds, contracts with entertainment industry companies, etc. A scholar is interested in finding out whether watching these programs frequently is related to believing that if people work hard, they can make it in life. They get a survey dataset where people report how many such shows they watch regularly (responses range from 0 to 10), and how strongly they believe that if a person works hard, they can make it (0 = do not believe this at all, 10 = believe this very strongly).

Below is a regression table that reports the results of their regression analysis using these variables.

	DV: Belief that hard work pays off
Number of rags-to-riches shows watched	0.23 (0.08)
Intercept	6.26 (0.83)
r2	0.04
Observations	1000

a) What is the coefficient on the number of shows watched? Interpret what this number means.

b) What is the intercept? Interpret what this number means.

c) Predict the expected level of belief that hard work pays off for a person who watches 3 of these shows regularly.

d) What is the standard error on the coefficient for number of shows watched?

e) Calculate the 95% confidence interval for this coefficient. What does the 95% confidence interval mean?

f) Interpret the r² value intuitively.

g) How many people did this survey include? Does this increase or decrease your confidence in the results?

h) Give two different explanations for why the scholar might find this relationship. Can this data analysis tell you which of these two explanations is more likely to be true? What could you do to try and find out which of your two explanations is the more accurate one?