00:01
All right, i put the data in here.
00:02
I had to write down all the x values in a row and all the y values in a row.
00:11
Now, i'm going to insert a scatterplot.
00:16
So, highlight the data, insert a scatter plot chart.
00:24
Sometimes it picks scatter plot, and i don't know why it does it sometimes, but not always.
00:34
There it is.
00:37
All right, now i want my x -axis to be x, and i want to remove that.
00:45
Now, these did have names.
00:47
What were the names of them? nope, they were just x and y.
00:51
Okay, they didn't have names.
00:56
Just trying to move this.
00:58
Okay.
01:01
Now, i want to insert an equation, i mean insert a trend line, and i'm going to show the, what am i doing here? i want to show the equation and the r squared.
01:22
What did i do by accident? number format was none.
01:39
What did i do? all right.
01:41
I'm just going to undo what i did because i don't like those numbers on there.
01:45
Okay, let's do this again.
01:53
I'm going to show the r squared value and use the equation.
01:57
Okay.
02:03
Draw the regression line by i.
02:06
Well, it's a little late for that.
02:10
I probably would have drawn something at least very close to that.
02:15
Place a star at each level.
02:21
About where the mean is located, does it look like this is the best line for the means? well, i would say the mean is right here.
02:35
So it's a little bit below.
02:38
Let's say the mean is, yep, right there.
02:40
Let's say the mean is a little lower.
02:43
I'd say the means a little lower.
02:44
I would say the means a little higher.
02:48
But does it look like it's the best for the means? yeah.
02:56
Calculate the equation of the regression line.
02:59
I have that right there.
03:05
Find the standard deviation of y about the regression line.
03:12
Okay.
03:15
Come on.
03:16
Just trying to move this.
03:20
So the distance of y from the regression line, well, first of all, i need to get my prediction.
03:28
That would be the regression line.
03:31
So equals negative 0 .415 times x plus 3 .79.
03:43
All right.
03:48
So there's a prediction.
03:51
Now i need the distance of y from that prediction.
03:55
So so this is just going to be y minus y -carat equals y minus y -carat.
04:09
All right.
04:14
Bring these all down here.
04:18
I can't, never mind.
04:22
Just need to bring it down to the bottom of the data here.
04:25
Okay.
04:27
So i need to find the standard deviation of y about the regression line.
04:34
So that would be this.
04:37
So i'm just going to do standard deviation like right here.
04:45
St -d -e -v of all of these things.
05:03
Now, is that the sample standard deviation? i don't know.
05:11
So i'm going to do a little investigation here.
05:17
It's st -d -e -v.
05:24
Estimates a standard deviation based on a sample, but it says i should use stdev point s instead.
05:35
So i'm actually going to change this.
05:37
It shouldn't really have any effect point s.
05:47
190512.
05:50
Same thing.
05:51
Gives me the same thing.
05:53
Okay.
05:55
Got it? now, construct a 95 % confidence interval for the true value.
06:06
Of beta 1.
06:08
All right.
06:09
So true value of the slope.
06:12
So the way that i've done this in the past is that i put in b0 and b1 over here.
06:22
So b0 is 3 .97.
06:29
What it said? 3 .79.
06:36
B1 is negative .415.
06:44
I think i'm reading these wrong.
06:47
3 .79 and 0 .79.
06:49
415.
06:51
Look again.
06:53
3 .79 .415.
06:55
All right, good.
06:59
Now, i want to make sure that this calculated the r value correctly.
07:03
I just put this in.
07:05
It says here that r squared is 0 .881 equals the square root of 0 .88.
07:16
Yep, that looks good.
07:19
Okay.
07:20
So i have my spreadsheet here, which i created.
07:26
Calculating the sum of squares of x, sum of squares of y, sum of squares of squares of xy, so that it can calculate r.
07:32
That's not real important right now.
07:35
It's not really in the context of this question...