Scenario:Data set presents a sample of the
number of defective flash drives produced by a small manufacturing company over
the last 30 weeks. The company’s operations manager believes that the number of
defects produced by the process is less than seven defective flash drives per
week. Please interpret and explain the regression
equation for predicting the number of defective flash drives over time (in
weeks), the correlation coefficient r and the coefficient of determination R2. Submit a Word document 1-2 pages
that include the following critical elements must be addressed:A. On
the spreadsheet attached – for the scatter plot comment on the strength and direction of
the association between the variables using the scatterplot.B. Correlation sheet
– check if this value supports the argument in part A.C .Regression sheet
– report the values of the intercept coefficient and the slope coefficient.
Interpret what these values indicate.D. Use
the regression output in part C and report the R2 value, interpret what this
value indicates.E .A
simple regression with one independent variable, the square of the correlation
coefficient equals the R2 value. Please verify that this relationship holds
true.F .Residuals
– Verify if the quantitative data condition, Linearity condition, outlier
conditions and equal spread condition are satisfied.
4_2_data_set___flashdrives.xlsx
Unformatted Attachment Preview
catter
Week
Week
1
Flashdrives 0,303005
Flashdrives
Correlation Coefficient R – .303
1
catter
Regression Statistics
Multiple R
0,303005183
R Square
0,091812141
Adjusted R Square 0,05937686
Standard Error
1,33523753
Observations
30
ANOVA
df
Regression
Residual
Total
Intercept
Week
1
28
29
SS
MS
F
Significance F
5,046607341 5,046607 2,830626
0,103603045
49,92005933 1,782859
54,96666667
Coefficients
Standard Error
t Stat
P-value
6,298850575
0,500010149 12,59745 4,69E-13
0,047385984
0,02816493 1,682446 0,103603
Lower 95%
Upper 95%
5,274626214 7,323074935
-0,01030726 0,105079229
Lower 95.0% Upper 95.0%
5,274626214 7,32307494
-0,01030726 0,10507923
Flashdrives
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Number of Defective Flashdrives for 30 W
6
7
6
5
7
5
6
6
8
9
9
7
5
6
8
8
9
7
6
8
5
7
6
8
10
9
7
8
7
6
Number of Defective flashdrives for 30 weeks
12
10
Flashdrives
catter
8
6
4
2
0
0
5
10
15
20
Week
25
e flashdrives for 30 weeks
y = 0,0474x + 6,2989
R² = 0,0918
Flashdrives
Linear (Flashdrives)
Linear (Flashdrives)
30
35
Flashdrives
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
6
7
6
5
7
5
6
6
8
9
9
7
5
6
8
8
9
7
6
8
5
7
6
8
10
9
7
8
7
6
y_hat
e+=y-y_hat
6,346
-0,346
6,393
0,607
6,44
-0,44
6,487
-1,487
6,534
0,466
6,581
-1,581
6,628
-0,628
6,675
-0,675
6,722
1,278
6,769
2,231
6,816
2,184
6,863
0,137
6,91
-1,91
6,957
-0,957
7,004
0,996
7,051
0,949
7,098
1,902
7,145
-0,145
7,192
-1,192
7,239
0,761
7,286
-2,286
7,333
-0,333
7,38
-1,38
7,427
0,573
7,474
2,526
7,521
1,479
7,568
-0,568
7,615
0,385
7,662
-0,662
7,709
-1,709
0,175
Residuals
Weeks
Weeks vs Residuals
3
2
Residuals
1
0
0
5
10
15
20
-1
-2
-3
Weeks
25
30
35
Weeks vs Residual
…
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