Statistics version 20 [SPSS]: IBM SPSS [Statistical Package for the Social Sciences] formerly called SPSS is a statistical software. There are several versions but we will use version 20. During the practical sessions you will use SPSS to define variables‚ enter data and carry out descriptive (Frequencies and Percentages‚ Mean +/- SD) and Inferential statistics (Chi-Square‚ ttest‚ etc.) Where can I get SPSS? You will need to contact Information Solution and Services (ISS) Why do I need to learn
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that explains the correlations among the set of variables. • To identify new‚ smaller set of uncorrelated variables to replace the original set of correlated variables in subsequent multivariate analysis. • To identify smaller set of salient variables from a larger set for use in subsequent multivariate analysis. In our research‚ we are using factor analysis to find out a smaller set of uncorrelated factors to replace the original correlated variables like fear of side effects‚ packaging‚ brand image
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Pattern Problem Hints 1. Program pat1.py. Variables: This program gets a number from the user. We need a variable to hold the number. We need to print the numbers from 1 up to the user’s number. We need a second variable for this. Program Plan: Get the user’s number. Convert it to a number. Initialize the print Number. What should the first number printed be? While the print Number is less than or equal to the user Number: print the user Number and increase it by 1. 2. Program
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coefficient (r) assesses the degree that quantitative variables are linearly related in a sample. Each individual or case must have scores on two quantitative variables (i.e.‚ continuous variables measured on the interval or ratio scales). The significance test for r evaluates whether there is a linear relationship between the two variables in the population. The appropriate correlation coefficient depends on the scales of measurement of the two variables being correlated. There are two assumptions underlying
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Independent Variables 4.7 Simple Linear Regression: Least Squares Method 4.8 Using the simple Linear Regression equation 4.9 Cautionary Notes and Limitations OBJECTIVES By the end of this chapter‚ you will be able to: 1 determine the strength and nature of the association between the variables. 2 interpret the observed relationship between the variables of interest. 3 estimate possible outcome of an unknown variable upon the knowledge of the value of the variables and the
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Explanation: Sales were above the company’s break-even sales and yet the company sustained a loss. The apparent contradiction is explained by the fact that the CVP analysis is based on variable costing‚ whereas the income reported to shareholders is prepared using absorption costing. Because sales were above the breakeven‚ the variable costing net operating income would have been positive. However‚ the absorption costing net operating income was negative. Ordinarily‚ this would only happen if inventories decreased
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THE AUTOCORRELATION PROBLEM Several approaches to data analysis can be used when autocorrelation is present. One uses additional independent variables and another transforms the independent variable. • Addition of Independent Variables Often the reason autocorrelation occurs in regression analyses is that one or more important predictor variables have been left out of the analysis. For example‚ suppose a researcher develops a regression forecasting model that attempts to predict sales of
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determining if a number is within a range‚ the OR operator would be best to use. 7. A flag is a variable that signals when some condition exists in the program. Algorithm Workbench 1. If variable x > 100 Then Set variable y = 20 Set variable z = 40 End If 2. If variable a < 10 Then Set variable b = 0 Set variable c = 1 End if 3. If variable a < 10 Then Set variable b = 0 Else Set variable b = 99 End If 4. If score < 60 Then Display “Your grade is F.” Else If score < 70 Then
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things must you normally specify in a variable declaration? You must specify the variable type and an identifier. int n; // type - int‚ identifier - n 6. What value is stored in uninitialized variables? Some languages assign a default value as 0 to uninitialized variables. In many languages‚ however‚ uninitialized variables hold unpredictable values. Algorithm Workbench 3. Write assignment statements that perform the following operations with the variables a‚ b‚ and c. Adds 2 to a and stores
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1 Topics on "Operational Research" Mar. 2007‚ IST Linear Programming‚ an introduction MIGUEL A. S. CASQUILHO IST‚ Universidade Técnica de Lisboa‚ Ave. Rovisco Pais‚ IST; 1049-001 Lisboa‚ Portugal Linear Programming is presented at an introductory level‚ mainly from the book by Hillier and Lieberman [2005]‚ abridged and adapted to suit the objectives of the “Operational Research” course. It begins with segments of its third chapter. Key words: linear programming; simplex method. I. Fundamentals
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