Preference
Maraya Bidjerano
12/1/2017
Step 1: Design the Experiment
We are not collecting data becayse we were given a set of data. Becuase of this, this step is minimal. We only need to determine how we will analyze this data. What tests we will use and what hypotheses we will test? We will address these questions in later steps.
Step 2: Load the Data
preference <- read.csv("preference.csv")
preference## preference primed
## 1 1.8 0
## 2 0.1 0
## 3 4.0 0
## 4 2.1 0
## 5 2.4 0
## 6 3.4 0
## 7 1.7 0
## 8 2.2 0
## 9 1.9 0
## 10 1.9 0
## 11 0.1 0
## 12 3.3 0
## 13 2.1 0
## 14 2.0 0
## 15 1.4 0
## 16 1.6 0
## 17 2.3 0
## 18 1.8 0
## 19 3.2 0
## 20 0.8 0
## 21 1.7 1
## 22 1.7 1
## 23 4.2 1
## 24 3.0 1
## 25 2.9 1
## 26 3.0 1
## 27 4.0 1
## 28 4.1 1
## 29 2.9 1
## 30 2.9 1
## 31 1.2 1
## 32 4.0 1
## 33 3.0 1
## 34 3.9 1
## 35 3.1 1
## 36 2.5 1
## 37 3.2 1
## 38 4.1 1
## 39 3.9 1
## 40 1.1 1
## 41 1.9 1
## 42 3.1 1Step 3: Describe the Data
There are 42 rows and 2 columns. The rows represent subjects surveyed and the first column indicatesthe preference of each subject regarding the logo and the second column represents whether the subjects have been primed about hte logo, 1, or not primed, 0.
Step 4: Identify the Purpose of the Study
The purpose of the study is to determine whether being primed before viewing a company’s logo has an impact on a consumer’s opinion of the logo.
Step 5: Visualize the Data
library(ggplot2)
ggplot(data=preference, mapping=aes(x=as.factor(primed), y=preference )) + geom_point()Step 6: Interpret the Plot
The plot suggests that the primed group has a greater mean preference
step 7: Formulate the Null Hypothesis
Null hypothesis: the mean of the primed group is the same as that of the preference group.
Step 8: Identify the Alternative Hypothesis
Alternative hypothesis: the mean of the preference group is not the same as that of the primed group.
Step 9: Decide on the Type of Test
A t-test is the best choice because it tests the hypotheses about population means of a quantitative variable. A proportion test is not recommended because it is best for testing hypotheses about population proportions of cateogrical variables. This is something we are not interested in for this particular project.
Step 10: Choose One Sample or Two
This is a two sample test because we have two independent groups - primed and preference.
step 11: Check Assumptions of the Test
We will use a qq plot to check the test’s assumptions.
ggplot(data=preference) + geom_qq(mapping=aes(sample=preference, color=as.factor(primed)))Step 12: Decide on a Level of Significance of the Test
the Level of Signficance will be 0.05.
step 13: Perform the Test
t.test(formula=preference~as.factor(primed), data=preference)
##
## Welch Two Sample t-test
##
## data: preference by as.factor(primed)
## t = -3.2072, df = 39.282, p-value = 0.002666
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.577912 -0.357543
## sample estimates:
## mean in group 0 mean in group 1
## 2.005000 2.972727Step 14: Interpret the P-value
Since the p-value is less than the level of significance, we reject the null hypothesis that the means are equal.
Step 15: Interpret the Confidence Level
The confidence interval is the range of approved values for the average difference. Zero is not included in this interval. Therefore, 0 is not a plausible value for the difference in means. It is not plausible that the means are the same.
Step 16: Interpret the Sample Estimates
We have concluded that the means are not equal. The mean is higher in the primed group for their preferences.
Step 17: State the Conclusion
Priming did help people improve their preference of product logos.