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Variations in Programs

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Since every program that is created is different from every other program, what are the variations that we look for and how do we control them?

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https://brainmass.com/computer-science/cpp/variations-in-programs-136673

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Even though every program that is created is different from every other program, there are known programming guidelines we can look for in every program.

General Programming Guidelines:
- Writing Clearly
- Writing Modular Code
- Breaking Complex Equations into Smaller Pieces
- Overloading Functions
- Making it Work, THEN Making it Fast
- Using Parenthesis
- Using Memory de-allocation
- Exception Handling
- Error Catching - assert and verify
- Using Goto Statements
- Using Templates
- Using Standard Template Libraries (STL)
- Using Variables
- Using Public Member Class Variables
- Using Functions
- Using Constant

* Writing Clearly
Just because you can do something in a single line of code doesn't mean you should. Do not write code just for the compiler; write the code for you and your fellow programmers. It may be you who returns to this code days, months, or years later and can't remember what that complex statement does. Never sacrifice clear code for "efficient" code.
Clearly written code is 'self' commenting, there should be no need for blocks of comments to describe what is going on. If that isn't the case, consider using more descriptive variable names, and breaking the code up into more distinct modules.

* Writing Modular Code
Code should be broken down into smaller pieces in order to make testing easier and to facilitate re-use of code. Functions that span several pages of printed text are hard to follow, harder to debug and are less likely to be reusable in alternative applications. As a general rule, a function should not span more than 2 pages (or 100 lines). Furthermore, two or more functions can be adapted by others for a wider range of uses than one single function.

* Breaking Complex Equations into Smaller Pieces
Complex equations containing many operators should be broken down into smaller pieces, with suitably name local variables to describe the individual portions of the code. This not only makes the code more understandable (self documented), but it is also easier to analyse the values of specific parts of the equation during debugging.

If only locally defined variables are used then writing code in this way is NOT less efficient - the code will run just as fast. However, it is important to constrain the scope of these local variables, which is achieved when you code in a modular fashion. To further aid the compiler you can encapsulate the locally used variables with { }.

// poor
double num=(A * 2 * cos(w * t)) * sin(k * x) * cosh(k * d) + 2 * B * sin(k * x - w * t);

// better
...
double num;
{
double Amp_A = A * 2 * cos(w * t);
double Wave_A = Amp_A * sin(k * x) * cosh(k * d);
doule Amp_B = B * 2;
Wave_B = Amp_B * sin(k * x - w * t);
num = Wave_A + Wave_B
}
...

* Overloading Functions
Overloading functions can be a powerful tool for creating a family of related functions that only differ in the type of data provided as arguments. If not used properly (such as using functions with the same name for different purposes) they can, however, cause considerable confusion. When overloading functions all variations should have the same semantics (be used for the same purpose).

* Making it Work, THEN Making it Fast
Often during development, developers want to get the most "bang" for their money. If you write code that works, and the interface is clear and correct, you can always go back later and make it faster. Strive to make it correct and readable first and fast second. Fast wrong code is still wrong code.

* Using Parenthesis
It is generally a good idea to use parentheses liberally in expressions involving mixed operators to avoid operator precedence problems. Even if the operator precedence seems clear to you, it might not be to others - you should not assume that other programmers know precedence as well as you do.

if(a == b && c == d) // avoid
if((a == b) && (c == d)) // good

x >= 0 ? x : -x; // avoid
(x >= 0) ? x : -x; // good

* Using Memory de-allocation
If you know that a pointer variable is ...

Solution Summary

The variations among programs and how to control those variations are explained.

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Calculate a 99 percent confidence interval for the mean debt-to-equity ratio. Using the Excel descriptive statistics output given below, find a 95 percent confidence interval for the mean of all possible yields obtained using catalyst XA-100. For each of the following situations, indicate whether an error has occurred and, if so, indicate what kind of error (Type I or Type II) has occurred. For each of the following sample results, determine whether the power plant should be shut down and the cooling system repaired. Define the null and alternate hypotheses using the treatment means M1, M2, and M3 to represent each group. Then test for statistically significant differences between these treatment means. Write the regression equation for the LaborCost (y) and BatchSize (x). What type of seasonal variation do you see in the sales data?

Q1 The bad debt ratio for a financial institution is defined to be the dollar value of loans defaulted divided by the total dollar value of all loans made. Suppose a random sample of seven Ohio banks is selected and that the bad debt ratios (written as percentages) for these banks are 7 percent, 4 percent, 6 percent, 7 percent, 5 percent, 4 percent, and 9 percent. Assuming the bad debt ratios are approximately normally distributed, the MINITAB output of a 95 percent confidence interval for the mean bad debt ratio of all Ohio banks is as follows:
Variable N Mean StDev SE Mean 95.0% CI
d-ratio 7 6.000 1.826 0.690 ( 4.311, 7.689)

a Using the sample mean and standard deviation on the MINITAB output, verify the calculation of the 95 percent confidence interval.

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c Banking officials claim the mean bad debt ratio for all banks in the Midwest region is 3.5 percent and that the mean bad debt ratio for Ohio banks is higher. Using the 95 percent confidence interval, can we be 95 percent confident that this claim is true? Using the 99 percent confidence interval, can we be 99 percent confident that this claim is true? Explain.

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a Using the Excel descriptive statistics output given below, find a 95 percent confidence interval for the mean of all possible yields obtained using catalyst XA-100.
b Based on the confidence interval, can we be 95 percent confident that the mean yield using catalyst XA-100 exceeds 750 pounds per hour? Explain.

Mean 811
Standard Error 8.786353
Median 814
Mode N/A
Standard Deviation 19.64688
Sample Variance 386
Kurtosis -0.12472
Skewness -0.23636
Range 52
Minimum 784
Maximum 836
Sum 4055
Count 5
Confidence Level(95.0%) 24.39488

Q3
Part X: For each of the following situations, indicate whether an error has occurred and, if so, indicate what kind of error (Type I or Type II) has occurred.
a We do not reject H0 and H0 is true.
b We reject H0 and H0 is true.
c We do not reject H0 and H0 is false.
d We reject H0 and H0 is false.

Part Y: What is the level of significance alpha? Specifically, state what you understand by an alpha value of 0.05 and how it is related to Type 1 error?

Q4 Consolidated Power, a large electric power utility, has just built a modern nuclear power plant. This plant discharges waste water that is allowed to flow into the Atlantic Ocean. The Environmental Protection Agency (EPA) has ordered that the waste water may not be excessively warm so that thermal pollution of the marine environment near the plant can be avoided. Because of this order, the waste water is allowed to cool in specially constructed ponds and is then released into the ocean. This cooling system works properly if the mean temperature of waste water discharged is 60°F or cooler. Consolidated Power is required to monitor the temperature of the waste water. A sample of 100 temperature readings will be obtained each day, and if the sample results cast a substantial amount of doubt on the hypothesis that the cooling system is working properly (the mean temperature of waste water discharged is 60°F or cooler), then the plant must be shut down and appropriate actions must be taken to correct the problem.

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1. Sample Mean = 60.482 and Sample Standard Deviation = 2
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(1) 29 randomly selected subjects were exposed to commercials shown in more involving programs,
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(3) 29 randomly selected subjects watched commercials only (note: this is called the control group).
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(1) 1.21
(2) 2.24
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Regression Statistics

Multiple R 0.99963578
R Square 0.999271693
Adjusted R Square 0.999198862
Standard Error 8.641541
Observations 12

ANOVA
df SS MS F Significance F
Regression 1 1024593f 1024593 13720.47k 5.04436E-17m
Residual 10 746.7624g 74.67624
Total 11 1025340h

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BatchSize(X) 10b 0.08662 117.13d 5.04436E-17e

For your aid, the different values in the ANOVA table are explained below using the superscript notation:
a: b0, b: b1, c: t for testing H0: b0 = 0, d: t for testing H0: b1 = 0,
e: p-values for t statistics, f: Explained variation, g: SSE = Unexplained variation,
h: Total variation, k: F(model) statistic, m: p-value for F(model)

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y = b0 + b1x

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H0: b1 = 0 can be rejected?

c What do you conclude about the relationship between LaborCost (y) and BatchSize (x)? Use the different test statistics provided in the data to support your case.

d. Interpret the meanings of b0 and b1. Does the interpretation of b0 make practical sense for this case? Think carefully about what the value of x will be when y = b0 .

e Estimate the value of LaborCost for a batch size of 10. Use your regression equation and show all your steps.

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All the data for answering the problems (a) through (c) has been provided to you. You do not have to compute any data for parts (a) through (c).

a. What type of seasonal variation do you see in the sales data? Is there no seasonal variation, constant seasonal variation, increasing seasonal variation, or decreasing seasonal variation? State your reasons. Find and identify the four seasonal factors for quarters 1, 2, 3, and 4.
b. What type of trend is indicated by the plot of the deseasonalized data?
c. What is the equation of the estimated trend that has been calculated using the deseasonalized data?
d. Compute a point forecast of tractor sales (based on trend and seasonal factors) for each of the quarters next year. You should show all your steps for each quarter forecast. (Hint: Note that you will use the equation from ( c ). This will provide you with the deseasonalized data. You then have to adjust it for the seasonal factor applicable for the quarter.)

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