Marketing Research MKT 3083
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This 29 page Class Notes was uploaded by Melyssa Schaefer on Thursday October 29, 2015. The Class Notes belongs to MKT 3083 at University of Texas at San Antonio taught by Yinlong Zhang in Fall. Since its upload, it has received 17 views. For similar materials see /class/231323/mkt-3083-university-of-texas-at-san-antonio in Marketing at University of Texas at San Antonio.
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Date Created: 10/29/15
Final Review 6 stribgtiion g39Learning guide Multiple choice questions 203 Problem solving60 crosstab testing differences regression Hygiothesis l39The null hypothesis is a statement about a status quo conservative statement the opposite to the alternative The alternative hypothesis is the opposite of the null The alternative hypothesis is a statement of what managers seek to prove nfc e le el is cars and the Truth Table 06 a Hypothesis testing Real Truth Null Hypothesis True False Don t Correct Decision reject with con dence Conclusion 1000 from fariability Variance and Standard Deviation zquot X yr Formula s i1 Examples our numbers 3454 how likely will you recommend DrZhang s course to your buddies 5 point scale Average 4 Deviation scores 1010 Square the above scores 1010 Sum of square terms 2 N13 Variance 23 Sqrtstandard deviation 08165 U39lhUJNH ION 39 7 There a 5gn cant relations7m between age and preferred type of car Nul amp Ater Sedan Coupe 501 Young 30 Not so Young 40 3912 Observed Expected i quot xi square value 39 Expected chisquare value is 0 when obs eq exp freq Expected frequency E Ri CJ39 if n R total observed frequency in the ith row Cj total observed frequency in the jth colqu n sample size r Not so Young Total 70 90 160 uencies Row Total 38tep 1 Com te ex Sedan Coupe 35 7080l60 35 45 Young 80 45 Not so Y Column Total 9039 quot ute Chisquare values Sedan Coupe Not so Young 40 40 Expected Sedan Coupe Young 35 45 Not so Young 35 45 ChiSquare sedan Coupe Young 071 055 Opserved 3 03 SW35 chlsquare Not so Young 071 1135 1328 1509 2321 3757 5089 Observed chisquare 252 Example 100 UTSA students 60 males 24 males mentioned going computer lab often 20 females mentioned going computer lab not often 60 males then 40 females Two possible answers for going computer lab often vs not often Why Example Null amp alternative hypotheses Often Not Total Often Male 24 36 Female 20 20 Total 44 56 continued 1 Expected frequency Male often 6044100264 male not often 6056100336 Female often 4044100176 female not often 4056100224 Step 2 Chisquare 1 2642426424264022 Chisquare 2 33636 33636336017 3033 4026 total098 Step 3 Df1 Step 4 Cannot reject the null difference withinsubjct 71quot llquot No difference in willingness to pay IquotIo 139 H2 0 Alter There is a difference in willingness to pay H1 Mr M2quot2 0 Step 1 For each of 400 respondents determ i difference in willingness to pay That is get 400 differences Differenc 11 01 0 17 07 issume we have mean difference 1 std deviation 5 3 Then compute standard error depend on sample size n 3 standard error i EM a5 3 Step 4 Calculate the tvalue for the observed mean difference of 1 and standard error of 015 observed difference 1 t value standard error 015 2 If the mean difference of 1 equivalj 39 39 394 tvalue of 667 is significantly differ zero then we could reject the ll hypothesis ii Pi F in 39 f gree of freedom n400139 t critical 196 for a 005 If tobserved is in the interval 196 19 4 it implies that the null hypothesis can 39 be rejected Example ll3939eairning is believing effect Before 3 4 5 4 how likely you are going to recommend Dr Zhang 17 After 5 4 6 5 Step 1 Null alternative managerial belief Step 2 Mean difference 1 Step 3 Difference scores 2 0 11 Continued Deviation of difference score 1100 4 Square term 1100 5 Sum 2 6 Variance 23 so standard deviation sqrt 230816 7 Standard error 081620408 8 T10408245 9 Df3 critical T318 2 tailed 235 ltaileid 10 Reject or not reject Endorser gtgt Mean Liking for a 65 755 product Sample std deviation s121 s222 Si 32 Sample Size n190 of consumer exposed to ad Scale 1 Dislike product 7 Like product 739 7S2 S2 7 quot Wandard error 1 2 pooled data quot1 n2 X1 X2 t observed S12 5 for n1 n2 2 df VW quot tentify Ind and Dep Variables Step 2 State your hypotheses Null LikingC s LikingL Alter Likingc gt LikingL This is a onetailed test Step 3 Set significance level Alpha 5 Step 5 Compare with tcritical For 198 n1 n2 2 degree of freedom t39critica 1645 tobservedgt fairca So reject Null hypothesis Step 6 Draw conclusion Product is better liked when ending Hilary Exiample IFWo commercials which is more effective 17 scale Commercial 1 40 respondents average45 standard deviation is Commercial 2 80 respondents average is 55 standard deviation is 4 Null and alternative Mean difference 1 Standard error sth22404480sqlt 01020548 T105481825 Df1202118 critical196 2 tailed 1645 1tailed Reject or not P EnrhWNquot PROBABILITY SAMPLING N ONPROBABILITY SAMPLING SAMPLE FRAME ERROR QUOTA SAMPLE SYSTEMATIC SAMPLING SKIP FORMULA STANDARD ERROR STANDARD ERROR OF THE MEAN DEGREE OF FREEDOM SAMPLE SIZE FORMULA FOR PERCENTAGE SAMPLE SIZE FORMULA FOR MEAN Each member of the population has a known probability of selection SIIlIPLE RANDOM SAMPLING the probability of being selected into the sample is known and equal for all members of the population probability Prsample size pop sizem N B 39 ethod m Numbers Method SYSTEIl1ATIC SAMPLING away to select a random sample from a directory or list that is much more efficient than with simple random samp ing CL USTER SAMPLING sampling in which the population is divided into subgroups called clusters Each one represents an entire population STRATIFIED SAMPLING separates the population into different subgroups or strata and the samples all ofthese subgrou s Personaljudgment is involved in drawing the sam le The probability of any member of the population being chosen is UN WN a riori Caused by the method ofthe sampling selection Caused by the size ofthe sample CONVENIENCE SAMPLES samples drawn at the convenience of the interview Error occurs in the form of members of the population who are infrequent or nonusers of that location JUDGEMENT SAMPLES samples that require a judgment or an educated guess as to who should represent the population Subjectivity enters in here and certain members will have a smaller chance of selection than others REFERRAL SAIMPLES NOWBALL SAMPLES samples which require respondents to provide the names of additional respondents mbers ofthe population who are less known disliked or whose opinions con ict with the respondent has a low probability of being selected QUOTA SAMPLES samples that use a speci c quota of certain types of individuals to be interviewed Often used to ensure that convenience samples will have desired proportion of different respondent classes the degree to which the sample frame fails to account for all of the population NOT COMPLETE UPDATE OF THE SAMPLE FRAME FAILING TO ACCOUNT FOR ALL OF THE DEFINED UNITS OF THE POPULATION establishes a specific quota or percentage of the total sample for various types of individuals to be interviewed They re appropriate when you have a detailed demographic profile ofthe population on which to base the samp e a way to select a random sample from a directory or list that is much more efficient than with simple random sampling the formula for SYSTEMATIC SAMPLING Skip Interval Population List Size Sample Size a measure of the variability in the sampling distribution based on what is theoretically believed to occur were we to take a multitude of independent samples from the same population 5 3 standard error 7 7 015 xE V400 SE Stande Error of Mean S Stande Deviation N Sample Size WWW annual l 39am Sample size formula for a mean H
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