In: Statistics and Probability
Please use mintab
The strength of a part was monitored as a function of temperature within a process. Generate a Scatter Plot, determine the value of the coefficient of correlation, generate a Fitted Line Plot, generate a Residual Plot, and estimate the percentage of variability in strength as a function of temperature. Note: Data divided into six columns for visual display. Your analysis should have a single Temp column and a single Strength column.
Temp |
Strength |
Temp |
Strength |
Temp |
Strength |
140.6 |
7.38 |
140.5 |
6.95 |
142.1 |
3.67 |
140.9 |
6.65 |
139.7 |
8.58 |
141.1 |
6.58 |
141.0 |
6.43 |
140.6 |
7.17 |
140.6 |
7.42 |
140.8 |
6.85 |
140.1 |
8.55 |
140.5 |
7.53 |
141.6 |
5.08 |
141.1 |
6.23 |
141.2 |
6.28 |
142.0 |
3.80 |
140.9 |
6.27 |
142.2 |
3.46 |
141.6 |
4.93 |
140.6 |
7.54 |
140.0 |
8.67 |
140.6 |
7.12 |
140.2 |
8.27 |
141.7 |
4.42 |
141.6 |
4.74 |
139.9 |
8.85 |
141.5 |
4.25 |
140.2 |
8.70 |
140.2 |
7.43 |
140.7 |
7.06 |
Using Excel, go to Insert Scatter Plot.
Using Excel, go to Data, select Data Analysis, choose Regression. Put Temperature in X input range and Strength in Y input range. Tick Residual Plot and Line Fit Plot.
SUMMARY OUTPUT | |||||
Regression Statistics | |||||
Multiple R | 0.977 | ||||
R Square | 0.955 | ||||
Adjusted R Square | 0.953 | ||||
Standard Error | 0.347 | ||||
Observations | 30 | ||||
ANOVA | |||||
df | SS | MS | F | Significance F | |
Regression | 1 | 71.054 | 71.054 | 588.593 | 0.000 |
Residual | 28 | 3.380 | 0.121 | ||
Total | 29 | 74.434 | |||
Coefficients | Standard Error | t Stat | P-value | ||
Intercept | 333.986 | 13.496 | 24.747 | 0.000 | |
Temperature | -2.324 | 0.096 | -24.261 | 0.000 |
Correlation coefficient (Multiple R) = 0.977
Percentage of variability in strength as a function of temperature (R-square) = 0.955 = 95.5%