1- A farmer wanted to find the relationship between the amount of fertilizer used and the yield of corn. He selected seven acres of his land on which he used different amounts of fertilizer to grow corn. The following table gives the amount (in pounds) of fertilizer used and the yield (in bushels) of corn for each of the seven acres.

a. With the amount of fertilizer used as an independent variable and yield of corn as a dependent variable, compute SSxx, SSyy, and SSxy. b. Find the least squares regression line.
c. Interpret the meaning of the values of a and b calculated in part b. d. Calculate r and r2 and explain what they mean.
e. Compute the standard deviation of errors.
f. Predict the yield of corn per acre for x = 105.
g. Construct a 98% confidence interval for ß.
h. Test at the 5% significance level if ß is different from zero....

...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...

...Home Depot Inc: Senior Management Report
Data was collected from CRSP daily observations for Home Depot starting January 1993 and ending December 2004. Observations for S&P and Home Depot were matched, and also for the T-Bill composite which is used as a substitute for the risk free rate. No unusual data patterns were observed during the work-up. After having done the Event Check, no large differences in the slopes of the data in the periods before and after 911 were discovered and both periods are used. The data matches the usual modeling assumptions and thus, results are to be expected to be interpreted without contradictions.
HD' Market Rating Analysis (MRA):
Jensen's Alpha ( ) was largely in the positive range. Therefore, Home Depot return was greater than the S&P return and HD outperformed the market. Beta ( ) was in the range of [1,1275959 to 1,0648879] and thus contained 1,0. So HD's risk/return profile is not much different from the market. (See appendix, p. 1)
The Market SPI was higher than the HD's Sharpe ratio, and therefore the market return is higher relative to total risk and the investor is better compensated for risk taken. On the other side, the TPI of the market was lower than Home Depots' Treynor ratio and thus, the company gives higher return relative to non-diversifiable risk (See Appendix, p. 1).
The BH-L Relative Unique Risk (RUR) was 47,8% which offers a moderate return for investors for risk taking (See Appendix, p. 2).
For...

...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...

...
Data Analysis
Descriptive Statistics, Estimation, Regression & Correlation
Treatment Effects of a Drug on Cognitive Functioning in Children with Mental Retardation and ADHD
Hossam Elhowary
MATH-1016-15
Dr. Maria DeLucia
December 09, 2014
Introduction
The purpose of this survey was to investigate the cognitive effects of stimulant medication in children with mental retardation and Attention-Deficit/Hyperactivity Disorder. Twenty four children were given various dosage of a drug a placebo and 0.60mg/kg. Variable descriptions are kind of drug taken and the number of correct responses after taking of the drug. They were on each dose one week before testing. This sample obtained from the preschool delay task of Gordon Diagnostic System (Gordon, 1983). However, does higher dosage lead to higher cognitive performance?
Histogram:
Box-and-whisker plot:
Multi plot:
Summary statistics:
Column
n
Mean
Variance
Std. dev.
Std. err.
Median
Range
Min
Max
Q1
Q3
Placebo
24
39.75
128.02174
11.314669
2.3095972
36
45
26
71
33
47
0.60
24
44.708333
151.7808
12.319935
2.5147962
42.5
48
29
77
35
54
Simple linear regression results:
Dependent Variable: .60 mg/kg
Independent Variable: Placebo
.60 mg/kg = 10.091611 + 0.87086093 Placebo
Sample size: 24
R (correlation coefficient) = 0.79980157
R-sq = 0.63968255
Estimate of error standard deviation: 7.5614248
Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat...

...Statistical Analysis for Quick Stab Collection Agency
Executive Summary
The purpose of the paper is to provide a statistical analysis of overdue bills for Quick Stab Collection Agency (QSCA). The data will be taken from accounts closed over a six month period. The goal is to determine if a correlation between the type of account, the amount of the bill and the days to collection exists. To determine the existence of a correlation, regressionanalysis of several variables was completed. This regressionanalysis also included predictions. Further study also included descriptive statistical analysis, together with graphs. This analysis will show that the correlation exists between the type of account and the days to collection. It will also show that the dollar amount the bill did not play a significant role in the days until collection.
Introduction
Quick Stab Collection Agency (QSCA) is a bill collection agency specializing in small less risky accounts. QSCA buys the rights to collect debts from the original owner of the debt at a significant discount. The right to collect a $50 debt may be purchased for as little as $10. This example would indicate a profit of $40 not accounting for costs of doing business. Based on this example QSCA will need to be selective when purchasing debt as there the potential for profit loss with non-payment.
In order for...

...
Chapter 10 RegressionAnalysis: Estimating Relationships
Formula for Correlation:
Slope in simple linear regression:
Intercept in simple linear regression:
Y is the dependent variable, and X1 through Xk are the explanatory variables, then a is the Y-intercept, and b 1 through bk are the slopes. Collectively, a the bs in the equation are called the regression coefficients.
Standard Error of Estimate:
R squared / R^2
General Linear Regression:
Regression line:
Sampling distribution of a regression coefficient has a t distribution with n-k-1 degrees of freedom:
ANOVA - total variation of a variable
The part unexplained by the regression equation:
The part that is explained:
SSR = SST - SSE
Point Prediction: Standard error of the prediction for a single Y:
Standard error of prediction for the mean Y:
Chapter 11, RegressionAnalysis: Statistical...

...Article analysis for Revenue Recognition Timing and Attributes of Reported Revenue: The Case of Software Industry’s Adoption of SOP 91-1 by Yuan Zhang
Timing of revenue recognition is a crucial part in revenue recognition. According to US GAAP, revenue should be recognized when it is realized/realizable and earned (FASB, 1984, Para. 83).
However, a number of software firms recognized revenue prior to product delivery or service performance in the past, which potentially violated one or both of the conditions of the revenue recognition principle. In response, AICPA released Statement of Position (SOP) 91-1 in Dec. 1991, which stipulated that if collectability is probable, license revenue should be recognized upon delivery and service revenue should be recognized ratably over the service arrangement.
The research question for this article is: How revenue recognition timing affects attributes of reported revenue? This question is interesting because: 1) revenue recognition timing is important in financial reporting and standard setters have devoted much attention, 2) very limited empirical research examining revenue recognition timing has been conducted, 3) software revenue recognition is unique as transfer of rights is achieved by license rather than on-the-spot sale of products.
The main hypotheses for this article and their intuitions are: 1) Early revenue recognition increases the timeliness of reported revenue. Its intuition is: early revenue...

...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...