Introduction: Satellite manufacturers recently proposed replacing battery technology with a silver-zinc technology. Since satellite applications require reliable and long-lasting battery technology, the manufacturing association requested an analysis of the following:
1. Develop a model for linear regression based on battery performance data, using the Log of (Cycles to Failure); the model should be based on the best predictors available to characteristic the behavior of the battery throughout its lifecycle;
2. Perform diagnostic analysis of the fitted model; and
3. Forecast the Cycles to Failure with a 95% confidence interval, using the model for the following independent variables: X1 = 1.5, X2 = 4.5, X3 = 50, X4 = 25, X5 = 2.
The table below provides the original battery performance data provided by the manufacturing association.
The Dependent Variable is:
- Cycles to Failure is the dependent variable (Y)
- The Log of (Cycles to Failure) is represented as Log(Y) The Independent Variables are:
- Charge Rate (X1)
- Discharge Rate (X2)
- Depth of Discharge (X3)
- Temperature (X4)
- End of Charge (X5)
Table 1: Original Performance Data
Cycles to Failure
Log Cycles to
Failure
Charge Rate (Amps)
Discharge Rate (Amps) Depth of Discharge (% of rated
ampere- hours)
Temperature (Celsius)
End of charge (Volts)
Data Y Log(Y) X1 X2 X3 X4 X5
1 101.000 2.004 0.375 3.130 60.000 40.000 2.000
2 141.000 2.149 1.000 3.130 76.800 30.000 1.990
3 96.000 1.982 1.000 3.130 60.000 20.000 2.000
4 125.000 2.097 1.000 3.130 60.000 20.000 1.980
5 43.000 1.633 1.625 3.130 43.200 10.000 2.010
6 16.000 1.204 1.625 3.130 60.000 20.000 2.000
7 188.000 2.274 1.625 3.130 60.000 20.000 2.020
8 10.000 1.000 0.375 5.000 76.800 10.000 2.010
9 3.000 0.477 1.000 5.000 43.200 10.000 1.990
10 386.000 2.587 1.000 5.000 43.200 30.000 2.010
11 45.000 1.653 1.000 5.000 100.000 20.000 2.000
12 2.000 0.301 1.625 5.000 76.800 10.000 1.990
13 76.000 1.881 0.375 1.250 76.800 10.000 2.010
14 78.000 1.892 1.000 1.250 43.200 10.000 1.990
15 160.000 2.204 1.000 1.250 76.800 30.000 2.000
16 3.000 0.477 1.000 1.250 60.000 0.000 2.000
17 216.000 2.334 1.625 1.250 43.200 30.000 1.990
18 73.000 1.863 1.625 1.250 60.000 20.000 2.000
19 314.000 2.497 0.375 3.130 76.800 30.000 1.990
20 170.000 2.230 0.375 3.130 60.000 20.000 2.000
When initially analyzing the performance data, the following observations were made concerning the Dependent Variable (Y) and its relationship with the Independent Variables (X1- 5):
- There is large variability in the original cycles to failure (Y) data. In the histogram of the dependent variable (Y), we can see that it is skewed toward the left. This could be problematic in conducting the regression analysis.
- When we conduct a probability plot for this data, the standard deviation is also very large.
These observations are displayed in the histogram and probability plot generated by Minitab below:
Figure 1: Histogram of Cycles to Failure (Y)
We would prefer a more normalized distribution for the dependent variable. When comparing the original dependent variable (Y) to the Log (Y), we do see some improvement in the distribution, indicating increased normality. The following observations were made when analyzing Log (Y):
- The standard deviation for Log cycles to failure is much smaller, but the P-value has decreased.
- In general, we would prefer to have a larger p-value in order to indicate greater normality of the distribution.
- At this point, it is difficult to discern the greater normality expressed by the Log (Y).
- For the purposes of this project (and to meet the client’s request), we will choose (Log cycles to failure) as the dependent variable for the regression model. Choosing the Log(Y) allows for clear interpretation in that constant changes to Log(Y) translate to constant percentage changes in Y.
These observations are displayed in the histogram and probability plot generated by Minitab below:
Figure 3: Histogram of Log Cycles to Failure (Log(Y))
CS 340 Milestone One Guidelines and Rubric Overview: For this assignment, you will implement the fundamental operations of create, read, update,
Retail Transaction Programming Project Project Requirements: Develop a program to emulate a purchase transaction at a retail store. This
7COM1028 Secure Systems Programming Referral Coursework: Secure
Create a GUI program that:Accepts the following from a user:Item NameItem QuantityItem PriceAllows the user to create a file to store the sales receip
CS 340 Final Project Guidelines and Rubric Overview The final project will encompass developing a web service using a software stack and impleme
