A customer service department asks its customers to rate their over-the-phone service on a scale of 1-20 immediately after their service has been completed. The department then matches each customer's rating, y, with the number of minutes the person waited on hold, x. The accompanying table shows the ratings and number of minutes on hold for 10 randomly selected customers. The regression line for the data is y=16.5445−0.3073x, x=4.7, ∑x2=285 and SSE=62.8456. Minutes Rating 3 14 8 13 1 18 4 10 6 15 2 17 9 15 4 20 7 15 3 14 α=0.05 t=-0.88 p-value= 0.406 Construct a 95% confidence interval for the population slope. UCL = LCL = (Round to two decimal places as needed.)
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
A customer service department asks its customers to rate their over-the-phone service on a scale of 1-20 immediately after their service has been completed. The department then matches each customer's rating, y, with the number of minutes the person waited on hold, x. The accompanying table shows the ratings and number of minutes on hold for 10 randomly selected customers. The regression line for the data is y=16.5445−0.3073x, x=4.7, ∑x2=285 and SSE=62.8456.
|
Minutes
|
Rating
|
|
|---|---|---|
|
3
|
14
|
|
|
8
|
13
|
|
|
1
|
18
|
|
|
4
|
10
|
|
|
6
|
15
|
|
|
2
|
17
|
|
|
9
|
15
|
|
|
4
|
20
|
|
|
7
|
15
|
|
|
3
|
14
|
α=0.05
t=-0.88
p-value= 0.406
|
UCL
|
=
|
|
|
LCL
|
=
|
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