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study guide for exam 1

by: odette antabi

study guide for exam 1 mkt302

Marketplace > University of Miami > Marketing > mkt302 > study guide for exam 1
odette antabi
GPA 3.6

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notes for exam 1
Marketing Research
Study Guide
MKT302, midterm1, exam, UM
50 ?




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This 4 page Study Guide was uploaded by odette antabi on Wednesday September 14, 2016. The Study Guide belongs to mkt302 at University of Miami taught by Tsiros in Fall 2016. Since its upload, it has received 21 views. For similar materials see Marketing Research in Marketing at University of Miami.


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Date Created: 09/14/16
MKT 302 Review of basic statistics Why use statistics  Increasing use of quantitative approach by science and business  More data collected, processed, and disseminated to public Descriptive statistics: present/summarize data Inferential statistics: predict markets share of a product not yet introduced in the market Nature of statistical data  Nominal: categorical data. Numerical in name only. Do not share properties of numbers. male/female, married/single. Data is different.  Ordinal: setting up inequalities is permitted. educational background. Information has a natural order  Interval: comparison are permitted but cannot multiply or divide. Temperature. The difference between categories is the same. Difference between 1-3 is the same as 81-83  Ratio: can form quotients, comparison of absolute magnitudes. weight. There is an absolute cero. Relative position with temperature you cannot say that 80 degrees is twice as hot as 40 because there is no absolute 0 Types of means:  Arithmetic mean  Geometric mean  Harmonic mean Measures of central tendency  Mean  Median  Mode Reliability: consistency. Measures and gives us the same answer each time. Validity: accurate Cross tabulation: examination of the responses to one question relative to the responses to one or more questions in a survey set Descriptive statistics: effective means of summarizing large sets of data Evaluating differences and changes:  Mathematical differences: if numbers are not the same they are different. Does not mean that the difference is important or statistical significant  Statistical significance: if a particular difference is large enough to be unlikely to have occurred because of the chance or sampling error, then the difference is statistically significant  Managerially important diffrences: we ,ust be able to distinguish between mathematically significant diffrences and statistical significant difference using the data nalyisi in managerial decision making  Hypothesis: an assumption, argument or theory that a researcher or manager makes about some characteristic of population under study Which analysis to perform? Depending on both variables that ypu are going to anlyze Steps in hypothesis testing 1. Stating the hypothesis  Null hypothesis: proven to be true. The same  Alternative hypothesis: another alternative proven to be true. The one we want to prove. If this is true we reject the null 2. Choosing the appropriate test statistics. Test of means, test or proportions, ANOVA…developing decision rule 3. . 4. . 5. . Type of errors in hypothesis testing:  type I error: H0 is true, and we reject H0. The person is innocent and we claim them guilty  type II error: H0 is false. Fail to reject H0. The person is guilty and we have not enough evidence to reject H0 and we set them free Bivariate analysis of association:  Bivariate techniques: statistical methods of analyzing the relationship between 2 variables Least square estimation: used to fit data from X and Y Significance value: takes out type I error. You want it to be no more than 5% for 95% confidence interval if it is less than 0.05 reject H0 R = explains what percent of the variables in the dependent variable is explained by the independent variable. The higher the better Conjoint analysis: technique that researchers and marketers use to understand how individuals value features (or attributes) of products or services by determining their trade offs between different levels of these features. Example: computer Processor 2  0 3 1 HD 200 0 300  1 Mem 2  0 4  1  You can create 8 different combinations with that numbers  The Y is going to be utility/ranking that is depending on each individuals choice  Only 3 coefficients in regression analysis Example: shoes. There are 3 levels we need to create dummy variables Sole Plastic, Poly, Rubber Sole 1 0 1 Sole 2  0 1 we always have 1 dummy variable less than options sole 1 sole 2 Plastic 1 0 when sole1 is 1  plastic Poly 0 1 when sole2 is 1 Poly Rubber 0 0 when both are 0  rubber 1,1 combination does not exist. It would be a combination of plastic and Poly that we don’t have calculate part-worth’s a11 – a13 = b1 a12 – a13 =b2 a11 + a12 + a13 = 0


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