Regression Analysis is a very effective quantitative forecasting technique for short, medium and long range time horizons and can be easily updated and changed. Regression Analysis: presupposes that a linear relationship exists between one or more independent (casual) variables, which are predicted to affect the dependent(target) variable. Linearity: The observed relationship between the independent and dependent variables Example: A HR can use regression analysis to predict the number of personnel required to perform the work.` Regression projects the future based on the past historical relationship between the independent and dependent variables Simple Regression Prediction model

General form of a simple LINEAR FUNCTION
Y=a+bX
This equation describes any straight line. The slope of the linear relationship between X and Y is represented by the letter b. The constant(y intercept) is represented in the equation by the letter a. The dependent(tatget)vatiable is represented by Y. The independent(casual) variable is represented by X. By using this formula we can determine the values of Y Warning: When you use a regression equation, do not use values for the independent variable that are outside the range of values used to create the equation. That is called extrapolation, and it can produce unreasonable estimates.. Example: SIMULATION MODEL/REGRESSION FORECAST

TARGET STORES STAFFING FORECAST MODEL
Y = 8 + .0011(X1) + .00004(X2) + .02(X3)
Y = Number of employees needed to staff the store
X1 = Square feet of sales space
X2 = Population of metropolitan area
X3 = Projected annual disposable income in millions of dollars Y = 8 + .0011(50,000sq ft) + .00004(150,000popul) + .00000002($850 million) Y = 8 + 55 + 6 + 17
Y = 86 employees needed at this store
Regression is a valuable forecasting technique and it enables us to plan and execute recruitment, selection, training and development programs in a planned, proactive fashion to ensure the trained...

...Quick Stab Collection Agency: A RegressionAnalysis
Gerald P. Ifurung
04/11/2011
Keller School of Management
Executive Summary
Every portfolio has a set of delinquent customers who do not make their payments on time.
The financial institution has to undertake collection activities on these customers to recover the
amounts due. A lot of collection resources are wasted on customers who are difficult or
impossible to recover. Predictive analytics can help optimize the allocation of collection
resources by identifying the most effective collection agencies, contact strategies, legal actions
and other strategies to each customer, thus significantly increasing recovery at the same time
reducing collection costs. A random sample of accounts closed out during the month of January through June will be used in determining if the size of the bill has an effect on the number of days the bill is late. The statistical analysis of the data involves the application of regressionanalysis. Based on the calculated value of correlation coefficient, there is no relationship between the size of the bill and the number of days to collect.
.
Introduction
The author was hired by the Quick Stab Collection Agency (QSCA) on a contractual basis to assist the company in auditing potential business in buying the rights to collect debts from its original owners. QSCA is a collection...

...
A. DETERMINE IF BLOOD FLOW CAN PREDICT ARTIRIAL OXYGEN.
1. Always start with scatter plot to see if the data is linear (i.e. if the relationship between y and x is linear). Next perform residual analysis and test for violation of assumptions. (Let y = arterial oxygen and x = blood flow).
twoway (scatter y x) (lfit y x)
regress y x
rvpplot x
2. Since regression diagnostics failed, we transform our data.
Ratio transformation was used to generate the dependent variable and reciprocal transformation was used to generate the independent variable.
3. Check if the model is adequate by checking the t-statistic, R2 and F-statistic.
F statistic reveals that the equation used to determine the relationship between the x and y is functional. Using the test statistic for the test of coefficients, it was revealed that the constant value in the equation is not significantly different from 0. Also, it was revealed that the transformed x, significantly explains the dependent variable. Also, it was revealed that the measure of proportion of variability explained by the fitted value is relatively high with 96.23%. This means that transformed data in blood flow explains 96.23% of the variation in the transformed data in arterial oxygen.
4. Check the normality of residuals and equal variances
predict r, resid
kdensity r, normal
pnorm tx
qnorm tx
rvpplot tx
Before we could perform the numerical test, we must first generate...

...correlated variables if one variable increase or decreases then other variable remain constant, correlation is said to be zero. For example, correlation between height and income.
ii. Multiple Correlations:
Correlation between more than two variables is called multiple correlation. For example, when we study the relationship between the yield of rice per acre and both the amount of rainfall and the amount of fertilizers used, it is problem of multiple correlation.
iii. Partial Correlation:
In partial correlation we recognize more than two variables but consider only two variables to be influencing each other, the effect of other influencing variables being kept constant. For example, in the rice production if we limit our correlation analysis of yield and rainfall to periods when the amount of fertilizers used existed, it becomes a problem of partial correlation.
iv. Linear and non-linear correlation:
The distinction between linear and non-linear correlation is based upon the constancy of the ratio of change between the variables. If the amount of change in one variable tends to bear a constant ratio to the amount of change in the other variable then the correlation is said to be liner otherwise non-linear. For example, observe the following two variables X and Y.
X: 10 20 30 40 50
Y: 60 120 180 240 300
It is clear the ratio of change between the two variables is the...

...business a picture of what the outcome could be both positive and negative outcomes.
3. Describe in at least two paragraphs the quantitative analysis approach, to include a high level overview of the importance of identifying the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing results, and implementation.
4. Respond to at least two other posts to receive full credit.
Assignment Week 2
Answer the following questions in 1 to 2 pages:
1. What benefit does a variable provide when developing and examining models?
2. Explain the purpose of simple linear regression and scatter diagrams. Please provide a simple linear regression model and define each variable used.
3. Describe multiple regressionanalysis and discuss potential uses for this model
4. Discuss the different types of forecasts to include time-series, causal, and qualitative models. When might a researcher or project manager utilize exponential smoothing? What benefit does a Delphi technique provide when working with qualitative-based decision making?
Respond to at least two other posts to receive full credit.
Assignment Week 3
Answer the following questions:
1. Discuss the importance of inventory control with respect to supply and demand.
2. What benefit can tools such as ABC analysis and just-in-time controls provide for an...

...CWRU
Regression Project Report
OPRE 433
Tianao Zhang 12/5/2011
Introduction
According to the data I’ve received, there are 6578 observations. The data base is composed by 13 columns and 506 rows. All the explanatory variables are continuous as well as the dependent variable and there are no categorical variables. My goal is to build a regression model to predict the average of Y or particular Y by a given X. 1. Do the regression assumptions such as Constant Variance, Normality and Independence and the correct functional hold for the model? By performing residual analysis, I can test the model. 2. Is there any relationship between the explanatory variables? I do multicollinearity test to test this condition. 3. I want to find out the confidence interval and prediction interval for the average Y and particular Y value. 4. In order to check the usefulness of the model and the relationship between X and Y, I consider several variables: i. Multiple Coefficient of Determination R2 and Radj2) ii. DWT iii. F Ratio iv. VIF value v. P Probability value.
Method of analysis
1. Find the important variables Use “Stepwise” to eliminate unimportant independent variables. Analysis—Fit Model—Stepwise After using “Stepwise”, JMP shows me that column 3 and column 7 should be deleted. So the rest of the columns have strong relationship with the dependent variables. 2. Checking VIF value If some...

...Delta Song Case Analysis
Possible cost drivers that will allow us to estimate a salary cost function for Delta are: available seat miles, number of departures, available ton miles, revenue passenger miles, and revenue ton miles. The two cost drivers we chose were revenue passenger miles and available ton miles. The salaries consist of payments to pilots, flight attendants and ticket agents. Their salaries are determined by the number of passengers and cargoes and the miles or hours flown. This is why we chose revenue passenger miles and available ton miles. After calculation we found that the R2 of revenue passenger miles is .1764, and the R2 of available ton miles is .5577. We used scatter plots to show this:
The available ton miles scatter plot shows a more linear relationship between the two variables. Low point (3132, 1145), high point (4029, 1514) Salary=0.4114xavailable ton miles-143.50
The greatest advantage about this technique is that it only uses two data so it is convenient. The disadvantages are that the data is inefficient. This is because the data is based on cost function for only two periods, meaning it is less accurate. Simple Regression Using simpler regression to estimate the salary cost with available ton miles as the cost driver. These are the results: Coefficients Intercept X Variable 1 -682.643 0.551693 Standard deviation 282.6033 0.79698
Salary= 0.5517x available ton miles- 682.63 R2=0.5577, and...

...RegressionAnalysis (Tom’s Used Mustangs)
Irving Campus
GM 533: Applied Managerial Statistics
04/19/2012
Memo
To:
From:
Date: April 19st, 2012
Re: Statistic Analysis on price settings
Various hypothesis tests were compared as well as several multiple regressions in order to identify the factors that would manipulate the selling price of Ford Mustangs. The data being used contains observations on 35 used Mustangs and 10 different characteristics.
The test hypothesis that price is dependent on whether the car is convertible is superior to the other hypothesis tests conducted. The analysis performed showed that the test hypothesis with the smallest P-value was favorable, convertible cars had the smallest P-value.
The data that is used in this regressionanalysis to find the proper equation model for the relationship between price, age and mileage is from the Bryant/Smith Case 7 Tom’s Used Mustangs. As described in the case, the used car sales are determined largely by Tom’s gut feeling to determine his asking prices.
The most effective hypothesis test that exhibits a relationship with the mean price is if the car is convertible. The RegressionAnalysis is conducted to see if there is any relationship between the price and mileage, color, owner and age and GT. After running several models with different independent...

...STA9708
RegressionAnalysis: Literacy rates and Poverty rates
As we are aware, poverty rate serve as an indicator for a number of causes in the world. Poverty rates are linked with infant mortality, education, child labor and crime etc. In this project, I will apply the regressionanalysis learned in the Statistics course to study the relationship between literacy rates and poverty rates among different states in USA. In my study, the poverty rates will be the independent variable (x) and literacy rates will be the dependent variable (y). The purpose of this regression is to determine if there is a correlation between the poverty rates and literacy rates in different states within USA. My null and alternate hypothesis are as follows:
Null hypothesis: Ho: β1 = 0 This hypothesis states that there is no correlation between the literacy and poverty rates
Alternate hypothesis: Ha: β1≠0 This is the hypothesis we want to prove, there is correlation between the literacy rate and poverty rates
The first step I did was to create a scatter plot for the data and the descriptive statistics study. The scatter plot shows a positive correlation between the two variables and the equation of the line is y = 1.0998x + 2.2613 with a R-square value of 0.5305. The scatter plot is shown below:
Figure 1: Scatter plot of relationship between poverty and literacy rates
Based on the coefficient of determination of 0.53,...