Discuss about the Confidence Interval and Statistical Significance.
The researcher examines the data set collected from an experimental study about footwear as per the physical aspects of the individuals such as gender, weight, shoe size and height. The considered data variables are – 1) “BodyVisualization_LOG” (it is the perceived body weight after each exposure captured by the user changing an image of a body until it compared the body weights for the individuals), 2) “GSR_Zscore” (galvanic skin response), 3) “Valence” (valence), 4) “Questionnaire_Speed” (speed perception), 5) “Questionnaire_Weight” (weight perception), 6) “Questionnnaire_Strength” (strength perception), 7) “Questionnaire_Straightness” (body straightness perception), 8) “Questionnaire_Vividness” (vividness of body feelings) and 9) “Questionnnaire_Surprise” (unexpected body feelings). The data analysis is based on the MS-Excel operation and measures extracted from the experiments with changing frequency and audio interface of the footwear at the time of witnessing the variations in the reaction of users. The participants acknowledged the high frequency, low frequency and controlled audio feedback from walking while wearing the prototype shoes. The researcher captured the perceptions of the participants about their body weight, mood, emotions and changes in behaviour involving three dimensions for each case of this experiment management.
The data cleaning helps to decrease wastage and consolidate the dirty or inaccurate data. A data analyst should focus on data cleaning for having correct information before using the data for analysis purpose. The analysis without cleaning the data may lead to a range of problems, linking problems, errors in parameter estimation, model mis-specification and linking biases. It may lead to draw a false conclusion. Data set in data sheet, the data set has a total of 21 variables. It is observed that- Sample no. 2 and 8 has missing values in the range “AD3” to “AF2”. Besides, sample no. 16, 11 and 10 also have some missing values. Sample no. 4 has lots of missing values starting from “L5” to “AC5”. The attempt of removing the missing values and eliminating empty cells is therefore carried out.
For single empty response of any sample, the analyst manually put the mean value of the column in the blank space. For the sample whose number of missing values are more than 3, the analyst has decided to neglect the whole sample from the data set. For example, the missing value of “GSR_Zscore” with high frequency of sample no. 2 is replaced by the average value of the entire “AD” column. On the other hand, sample no. 4 is removed from the whole data set.
The response of these people is gathered as per questionnaires of the survey. To have descriptive statistics, the study depicts that out of 21 samples, 17 are females and 4 are males.
Among 21 chosen samples, GSR_Z-score has higher average score for high frequency followed by low frequency and control frequency. The average of Z-scores in high audio frequency, low audio frequency and controlled audio frequency are 0.127, (-0.140) and (-0.024) respectively.
The average GSR Z-score is highest for high frequency followed by controlled and low audio frequencies. The estimated GSR ranges maximum for controlled frequencies.
The positive Valence response are the responses that are greater than 5. Among 21 chosen samples, 17 samples have positive valence rate in high audio frequency, 11 samples have positive valence rate in low audio frequency and 13 samples have positive valence rate in control audio frequency. The proportions of Valence response in high audio frequency, low audio frequency and controlled audio frequency are 0.81, 0.52 and 0.62 respectively.
The positive perceived proportional response of Straightness are the samples that have experimental measure from 4 to 7. The proportion of positive perceived proportional response of Straightness is maximum for “High” audio frequencies and minimum for “Control” audio frequencies. The positive response of perceived Straightness of “High” audio frequency differs in the range of proportion 0.78 and 1.0 [3]. The predicted response of “Control” audio frequency ranges within the proportion 0.41 to 0.83.
The bar chart depicts that “Straightness” has higher proportion in case of high frequency followed by the proportion in case of low frequency. The proportion and estimated ranges are lowest for control frequency management.
The positive perceived surprise response are the samples that have experimental measure from 4 to 7. The proportion of positive surprise feeling is higher for “High” audio frequencies and lower for “Control” audio frequencies. The positive surprise response of “High” audio frequency varies from the range of proportion 0.31 and 0.74. The predicted response of “Control” audio frequency ranges within the proportion 0.17 to 0.58.
The bar chart refers that the positive surprise proportions and its 95% estimated ranges of proportions are ordered in the way High, Low and Control.
Research Question: Do the positive response of the three variables “Speed”, “Weight” and “Strength” are equal with respect to the three types of audio frequency that are High, Low and Control?
The proportions of “Speed” refer that-
The proportions of “Weight” refer that-
The proportions of “Strength” refer that-
The bar chart shows that speed perception is higher in high frequency than low or control frequency. The weight perception is higher in low audio frequency rather than control or high frequency. Lastly, the straight perception is maximum for high frequency followed by low or control frequency. Hence, the positive perception rate as per three types of frequencies do not follow same pattern for all the three variables that are “Speed”, “Weight” and “Strength”.
Reference List:
J. Bartlett, J. Kortlik and C. Higgins, “ProQuest Statistical Abstract of the USA2014 134 ProQuest Statistical Abstract of the USA Ann Arbor, MI ProQuest 2013-“, Reference Reviews, vol. 28, no. 4, pp. 22-24, 2014.
J. Gandhi, “Political Institutions under Dictatorship”, Langtoninfo.com, 2018. [Online]. Available: https://www.langtoninfo.com/web_content/9780521897952_frontmatter.pdf.
S. Nakagawa and I. Cuthill, “Effect size, confidence interval and statistical significance: a practical guide for biologists”, Lira.pro.br, 2018. [Online]. Available: https://lira.pro.br/wordpress/wp-content/uploads/downloads/2012/03/nakagawa-e-cuthill-2007.pdf.
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