00:01
00:05
00:07
00:12
00:19
00:30
00:36
00:43
00:50
00:56
01:00
01:05
01:10
01:25
01:31
01:37
01:42
01:48
01:52
01:56
02:04
02:07
02:26
02:30
02:40
02:49
02:53
02:58
03:01
03:13
03:17
03:26
03:30
03:37
03:45
03:52
04:01
04:07
04:20
04:27
04:33
04:39
04:43
04:52
04:58
05:03
05:08
05:16
05:22
05:32
05:37
05:50
05:56
06:00
06:02
06:14
06:27
06:36
06:47
06:52
07:03
07:08
07:17
07:29
07:32
07:37
07:47
07:57
08:03
08:15
08:18
08:23
08:29
08:32
08:38
08:45
08:54
09:10
09:15
09:21
09:24
09:31
09:42
09:47
09:58
10:00
10:10
10:14
10:18
10:28
10:36
10:39
10:47
10:53
10:56
11:01
11:08
11:22
11:30
11:34
11:39
11:42
11:45
11:49
11:51
11:57
12:16
12:26
12:32
12:41
12:45
12:49
12:56
13:04
13:09
13:17
13:23
13:29
13:33
13:41
13:44
13:51
13:54
14:02
14:06
14:17
14:25
14:37
14:44
14:47
14:54
14:58
15:08
15:14
15:19
15:27
15:31
15:45
15:52
15:55
16:18
16:20
16:26
16:30
16:33
16:41
16:47
16:52
16:57
17:02
17:09
17:12
17:28
17:36
17:40
17:46
17:57
18:04
18:12
18:18
18:21
18:27
18:36
18:40
18:49
18:54
18:58
19:04
19:06
19:16
19:29
19:37
19:42
19:47
19:51
19:59
The lecture Linear Regression by David Spade, PhD is from the course Statistics Part 1. It contains the following chapters:
Which of the following is not true of the linear model?
Which of the following is not true of the relationship between linear regression and correlation?
Suppose the variables X and Y are linearly related through the regression equation Y= 12.5  0.275 X. What do we know from this equation?
Which of the following is not true of the R² quantity?
When we look at a scatterplot, what indicates a lack of violations of these assumptions?
Suppose the relationship between age and earnings could be represented by the linear regression equation Earnings = 100 + 0.8*Age. What would be the estimated earnings of a person whose age is 43?
Suppose the relationship between age and earnings could be represented by the linear regression equation Earnings = 100 + 0.8*Age. The actual earnings of a person aged 43 are $150. What is the residual error?
Suppose the correlation between two variables is 0.75, standard deviation of y variable is 5 and the standard deviation of x variable is 2.5. What is the slope of the regression line?
Suppose the slope of regression line is 2 and Y variable is 10 when X variable is 3. What is the value of the intercept of the regression equation?
If a rise in health expenditure leads to a positive linear rise in GDP, then which regression equation is likely to represent their relationship?
5 Stars 

5 
4 Stars 

0 
3 Stars 

0 
2 Stars 

0 
1 Star 

0 