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Marketing research tools - Assignment on Factor and Cluster Analysis

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AMDA CLASS ASSIGNMENTS COLLATED REPORT Submitted to Prof. Prahlad Mishra Submitted By U108045 - Rahul Nishit U108057 - Tanya Gupta U108095 - Rahul Singh U108097 - Richa Gupta U108109-Sourabh Choudhury U108111 - Sujit Sahoo Contents Assignment on Factor and Cluster Analysis 3 Variables Used 3 Factor Analysis 4 Cluster Analysis: Approach 1 8 Cluster Analysis: Approach 2 12 Cluster Analysis: Approach 3 15 Assignment on Heteroscedasticity 21 Assignement on Mulicollinearity: 25 Assignment on Autocorrelation: 33 Assignment on Independent Dummy Variable ( Cross Section Data) 35 Assignment on Dummy Variable (Time series data and structural stability/instability) 40 Assignment on LPM, Discriminant and Logit Analysis 46 LPM 46 Discriminant Analysis 47 Logit Model: 49 Assignment on Eview - Panel Data 52 Assignment on Factor and Cluster Analysis Variables Used The 11 variables that were decided upon are as follows: Influence of Brands: In a retail superstore, there is availability of a large number of brands, all under one roof. There may be situations where the consumers get allured by the presence of large number of brands and thus affect his/her buying decision. (Code-B) Product Offering (Variety & Range): It is generally perceived that customers get attracted the huge number of products that are offered by these shopping stores. So we tend to measure as to whether he really prefers this variety and does this influence his buying decision. (Code-C) Trust on Product Quality: There are instances when the products offered in organized retail outlets are of unknown brands or they are in house brands. So we try to assess whether customers tend to trust these brands just because they are present inside the reputed store, even without knowing anything of that particular product quality. (Code-D) Brand Comparison within products: Generally a big retail store offers a wide portfolio of brands for the same product category. We try to measure that whether the consumers shop from such stores because he/she has the options of comparing the brands within the same brand. ...read more.


Coefficientsa Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) 2.421 9.625 .251 .803 lnnewx .266 1.645 .025 .162 .872 a. Dependent Variable: lnui2 Remedial Measure Employed 1.Based on the analysis made above to remove Heteroscedasticity, since the Error Variance is proportional to Xi(Revenues) data transformation would be done as follows. Profits= Pi, Revenues = Ri Pi/Sqrt(Ri) = B1/sqrt(Ri) + B2 Sqrt(Ri) + Ui/Sqrt(Ri) Regression output on the transformed dataset. Yi = -16.281 + 0.124*(Xi) Analysis of the Estimated Equation The Estimated Equation when checked for out for sample data the error variance came down from 7% to 3.4% (Refer to the excel sheet for calculations) Assignement on Mulicollinearity: Objective: To check if there is any Multicollinearity in the explanatory variables of the chosen data set. If it exists then provide a remedy to remove the econometric problem. Data Set: Time Series Data from the American Federal Database has been taken for Data Analysis. The monthly data of the following variables runs from 2004 January to 2009 June ie (12*5 + 6 = 66 data points) The regression is run on the 62 data points and 4 data points are left for the purpose of estimation. 1.Average Hourly Earnings: Manufacturing Sector (earnings) 2.Average Weekly Hours: Manufacturing Sector(hours) 3.All Employees: Durable Goods Manufacturing(empindur) 4.All Employees: Non Durable Goods Manufacturing(empinnondur) Apriori Reasoning: For a time period, Hourly labor earnings in a sector would be driven by the Number of Labors Employed in that sector and the Labor Hours put by them. It is assumed that if more labor hours are being put then the hourly earning would go up. Similarly if the number of people employed goes up for a given industry conditions(industry fortunes remains stable) then the hourly earnings would come down else vice versa. This reasoning is applied to the Manufacturing Sector in USA. The manufacturing sector is broken down into Durable goods and Non Durable goods producing sector. ...read more.


dependent var 1304.850 S.E. of regression 226.1971 Akaike info criterion 13.98676 Sum squared resid 665146.6 Schwarz criterion 14.57182 Log likelihood -162.8345 Hannan-Quinn criter. 14.14903 F-statistic 71.42302 Durbin-Watson stat 1.305123 Prob(F-statistic) 0.000000 Analysis: PROFIT= -90.83+ 0.06*SALES+ 0.40*PERSONNEL_COST- 0.004*ASSETS Sig: (.64) (.07) (.93) Here we see that adjusted R square is 97% which is sufficiently high. The coefficient for personnel cost is significant and for the rest of the two independent variables it is not constant. This is in accordance with our apriori reasoning. Thus we can say that profit in IT companies is mainly derived from personnel cost and is not dependent on assets and sales. Here we must be careful in analyzing as the coefficients which we are seeing are normalized coefficients as they contain the effect for fixed effect. e) Random effect: using the normalized function Dependent Variable: PROFIT Method: Panel EGLS (Cross-section random effects) Date: 09/18/09 Time: 15:39 Sample: 1 5 Periods included: 5 Cross-sections included: 5 Total panel (balanced) observations: 25 Swamy and Arora estimator of component variances Variable Coefficient Std. Error t-Statistic Prob. PERSONNEL_COST 0.268060 0.174164 1.539126 0.1387 SALES 0.159497 0.098940 1.612051 0.1219 ASSETS -0.058421 0.043510 -1.342686 0.1937 C 42.33998 256.0015 0.165390 0.8702 Effects Specification S.D. Rho Cross-section random 525.1139 0.8435 Idiosyncratic random 226.1687 0.1565 Weighted Statistics R-squared 0.962141 Mean dependent var 346.8527 Adjusted R-squared 0.956733 S.D. dependent var 1011.933 S.E. of regression 210.4891 Sum squared resid 930419.0 F-statistic 177.8990 Durbin-Watson stat 1.451791 Prob(F-statistic) 0.000000 Unweighted Statistics R-squared 0.937323 Mean dependent var 1833.841 Sum squared resid 2561179. Durbin-Watson stat 0.527403 Analysis: PROFIT= 42.33+ 0.16*SALES+ 0.27*PERSONNEL_COST- 0.06*ASSETS Sig: (.12) (.14) (.19) Here we see that adjusted R square is 96% which is sufficiently high. The coefficients for none of the independent variables are coming to be significant. This is not in accordance with our apriori reasoning. We cannot conclude anything concretely form this. This could be due the normalized effect contained in the coefficients because of random effect. Other reason could be data insufficiency. ?? ?? ?? ?? AMDA CLASS ASSIGNMENTS COLLATED REPORT AMDA Final Report Page 2 AMDA CLASS ASSIGNMENTS COLLATED REPORT ...read more.

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