Question

According to the mere exposure effect, the more familiar an item is, the more people tend to like it. Think about commercials, which are often based on repetition! Imagine a researcher tracks the TV viewing habits of participants over a one week period, keeping track of how often participants see a specific advertisement for Product A. The researcher then asks participants to rate how much they like Product A on a scale ranging from 0 (does not like it at all) to 20 (likes it very much). Using Number of Ads (X) and Rating of Liking (Y), fill in the blank spaces in following chart to assess thi relationship: (Note: Complete this chart offline. Then, answer questions 1 through 5) Number of Ads (X) Rating of Liking (Y) X2 Y 2 X Y 3 3 5 MARR T3 7NNNN 12 4 15 5 16 7 1 18 8 9 10 Total EXY - (ZXZY /n EY - bEX Ex? -[(EX)? /n] 1) 4 1 / 1 TI TUTTI UNULUI 1 20 14 11 LIT

Answer 1

solution. S that o Given xy her of Rating of x² ² liking (Y) - Number of Ads (x) كم) و 1 9 5 a 25 9 49 16 144 16 225 16. 25. 256 لد مو ر 180 14 18 .49 324 126 12 64 144 96 20 8 400 100 196 140 34-54 EY=122 Ex=370 EY²1772 EXY=76.9 Total numbers. N=10 Regressions toeight koett NEXY-EX.ET Aby zb= NEX² - (ext - 1102 - 10x 769 .-548122 184 Option B LOX370 - Suary Regression weight cofth - 2406 (three decinal) The coure correct option is B The Predictor variable is number of advertisements The criterion variable is amount of liking. The researchers are trying to see if the no. of als predicts the amount of CakuЯ

© ' regression interceptia) Ex-54 5x122 Ex?-370 Suryana EXY = 769 numbers = 10 a= EY.EX²-{XEXY NEX²({x} 122 X370-548769 10x370-54 - 45140-41526 784 options = 4.6097 Regression intercepta) 4.609 (three decimal ) Regression ean of (Y) Ex = 54 EY=122 e mean x 25.4 cm (my) I mean y =12.2 Sum of squares = 78.4 (sx) Sum of products = 1182 (SP) Regress,m esu ġ bx+a no SP = 110.2=1.40561 - SSX ff. u a = MX-MX = 12:2–(l. 40145-4) Û = 1.405614+4.60969 option = 4.60989
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