Question

1) In your own words, explain the differences parametric and nonparametric tests. Also noted which kind is preferred to be used.

2) Pictured below is an obvious example of correlation vs. causation. The sun causes ice cream to melt. The sun also causes people to get sunburns. However, melting ice cream does not cause sunburns and vice versa; instead, those variables are correlated with one another. Provide another obvious example of correlation vs. causation. You may not use an example from any of the lectures/videos this semester.

3) In your own words, what is the “line of best fit” in regression analysis?

4) What does the “r-square” number in regression analysis tell us?

1. It tells us what percentage change in the DV can be explained by the IV.
2. It tells us that as 1 unit of the IV changes, how much the DV does.
3. It tells us whether the statistic is significant or not.
4. It tells us whether the variables are correlated or not.

Answer 1

ans 1 =

parametric tests assume underlying statistical distribution in the data where as nonparametric tests do not rely on any distribution

ans 2=

correlation between ice cream sales and sunglasses sold ,causation takes a step further than the correlation . it says any cgange in the value of one variable will cause a change in the value of another variable which means one variable makes other to happen it is also reffered as cause and effect.

ans 3=

a straight line will resul from a simpler linear regression analysis of two or more independent variables.

ans 4=

r square number in regression analysis

the coefficient of multiple determination for multiple regression, 100% indicates that the model explains all the variability of the response data around its mean.