A bank records the number of mortgage applications and its own prevailing interest rate (at the first of the month) for each of 16 consecutive months. Is there a correlation between the interest rate (x) and the number of applicants (y), and if so, is it significant at the 5% or the 1% significance level Answer Choices -0.733, significant at 1% -0.652, no significant correlation -0.587, significant at 5% -0.587, significant at 1% -0.652, significant at 5%
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.
![Interest
9.5
9.9
10.0
10.5
11.0
11.5
11.0
12.0
Number
27
29
25
25
19
20
17
13
Interest
12.0
12.5
13.0
13.5
13.0
12.5
11.5
11.5
Number
15
10
10
20
24
27
29
a = 0.05 a = 0.01
4
0.950
0.999
0.878
0.959
6.
0.811
0.917
0.754
0.875
8
0.707
0.834
9.
0.666
0.798
10
0.632
0.765
11
0.602
0.735
12
0.576
0.708
13
0.553
0.684
14
0.532
0.661
15
0.514
0.641
16
0.497
0.623
17
0.482
0.606
18
0.468
0.590
19
0.456
0.575
20
0.444
0.561
25
0.396
0.505
30
0.361
0.463
35
0.335
0.430
40
0.312
0.402
45
0.294
0.378
50
0.279
0.361
60
0.254
0.330
0.236
0.220
70
0.305
80
0.286
90
0.207
0.269
100
0.196
0.256
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