1 2 CLO Business Decision Making Project Part 1 I want to understand the buying pattern of Lay's chips in the organized snack food industry, observed across various socio-economic segments of society in terms of their frequency, channel and other behavioral aspects which affect their buying pattern for this category. I want to test whether sales of chips depends on brand, availability, price, quantity, price spend on Advertising (Ad), occupation, and flavor. Also how these factors affect the buying pattern. The research will help me study the consumer behavior towards the product (Lay's) in the presence of other players like Ruffles and Pringles who are providing a similar product at the same price points. The geographical scope of this study is global. It was made sure that the samples are random. The social networking website, Facebook was used to record the responses of the questionnaire. My dependent variable is \"sales of chips\". While the independent variables chosen are namely brand of chips, availability, price, quantity, the price spent on advertising, responses on occupation and flavor of chips. The questionnaire was designed to identify the impact of each of these independent variables on the buying pattern of potato chips by consumers. There is a tremendous amount of responses I received regarding my questionnaire. In this case we have implemented a non -probabilistic convenience sampling method. The subjects are selected just because they are easiest to recruit. This method is less time consuming, more cost effective and the subjects are easily available. Brand, occupation and flavors are measured on nominal level of measurement as they are categorical in nature. The interval level of measurement has equal differences between scale values and equal quantitative meaning. It does not have a true zero point. A true zero point 3 means that a value of zero on the scale represents zero quantity of the construct being assessed. Availability, price, quantity, promotion and ads are measured on an interval scale. In all forms of research, it would be ideal to test the entire population, but in this case, the population is just too large that it it's physically impossible to include every single individual. Thus I relied on a sampling technique such as convenience sampling. I asked my friends on my Facebook to please take a moment to complete the questionnaire and also asked them to share with their friends to also complete. By doing this I collected 120 responses, and now applied regression analysis using statistical software SPSS. Regression model is obtained after running regression analysis by taking sales of chips as the dependent variable and factors like brand, availability, price, quantity, price spent on advertising (Ad), Occupation, and flavor as independent variables. The regression model thus obtained will help me make inferences related to my research. If the model is significant, I can use interpretations and give suggestions with the help of coefficients. While doing my primary research, one of the main constraints is that it is not possible to consider responses of the whole population. The questionnaire was circulated on a social media platform like Facebook, and the responses were collected using a Google online form. The survey instrument I used was a questionnaire that comprised of six questions. Questionnaires have many advantages such as being inexpensive and also not requiring much effort as opposed to a verbal survey. The collected responses were cleaned of inconsistencies and redundancies so as to obtain data which can be then subjected to our analysis without giving erroneous results. I take my level of significance, \"alpha\" as five percent. And thus my confidence level is ninety five percent. Since Type I error is considered to be more serious as compared to type II error. We want to control alpha (Type I error) at a predetermined low level which is 5%, and to achieve this we maximize the power (1 - beta) or minimize (beta) Type II error. 4 Summary Introduction The research study is arranged for the study of the buying pattern of the lay's chips in the organized snack food industry.The role of the statistical data analysis is very important in solving any business related problem. By using the data generated in the industry or business, one could analyze this data and draw the conclusions regarding the business problems. We want to analyze the data related to the business industry by using the different statistical tools and techniques. We have to summarize the statistical methods for the analysis of the data for lay's chips in the organized snack food industry. By using this data analysis, the management team can take better decisions for the overall growth of the industry. Let us see this statistical data analysis in detail given in the next parts. Data Collection A data is collected from the customers of the lay's chips. For the data collection, the survey method is used. The online survey is conducted by using the social networking sites such as Face book. The data is collected from the respondents for the different variables such as brand of chips, availability, price, quantity of chips, flavor of chips, etc. The data is collected for 120 respondents. Data Analysis In the data analysis part, we have to discuss the different methods required for the analysis of the data regarding the lay's chips in the organized snack food industry. For data analysis purpose, we need to use the different statistical methods and techniques for making inferences about the different variables regarding the lay's chips in the organized snack food industry. In this data analysis part, we have to see the descriptive statistics, inferential statistics, probability and trend analysis, linear regression, time series analysis etc. Let us see these methods or buying pattern of lay's chips in the organized snack food industry in details described as below: Descriptive Statistics By using the descriptive statistics for the different variables under study, we get the general idea about the data. We know that the descriptive statistics consist of the study of the mean, mode, median, standard deviation, variance, minimum, maximum, range, kurtosis, etc. All these measures are useful for the comparison purpose. We can compare the data easily by calculating the descriptive statistics. For this study, we need to calculate the descriptive statistics for brand of chips, availability, price, quantity of the lay's chips in the organized snack food industry. We can calculate the average monthly sale of the lay's chips and also we can calculate some descriptive statistics for the sale of the lay's chips. By using this data we can compare the sale of the lay's chips with other competitor's chips. Inferential Statistics Inferential statistics or testing of hypothesis plays an important role in checking or testing different claims under study. We can test any research question or hypothesis or claim by using the different statistical tests. If we have the previous information about the population parameters about the data regarding the buying pattern of the lay's chips in the organized snack food industry, we can use the one sample or two sample z tests for checking the hypothesis or claims under study. If we do not know the population information about the data, then we can use the t tests which are entirely based on the sample data. So, we can use the t test for checking the claims. We can use this t test for checking the claim whether the average sale for the lay's chips for different years were same or not. In case we need to test for more than two samples, then we can use the one way analysis of variance or ANOVA test for checking the claim whether there is any significant difference exists in the different types of lay's chips in the organized snack food industry. Probability and trend analysis By using the probability assumptions, we can find out the different possibilities regarding the lay's chips in the organized snack food industry. Trend analysis also plays an important role while carried out the statistical analysis. The trend analysis explains the pattern of variation and it gives us the idea about the variation pattern and based on this variation pattern the management team could take better decisions for the future purpose. By using the trend analysis, we can find out seasonality, variability and stability of the data regarding the lay's chips in the organized snack food industry. Linear regression for trend analysis For the future estimation for the response variable sales of the lay's chips or prediction of the values for the variable under study, we can use the linear regression. By using the independent variables such as brand of chips, availability, price, quantity etc., we can develop the linear regression equation for the dependent variable sales of lay's chips and then using this regression equation, one can easily find out the estimated values for the dependent variable sale of the lay's chips. Time series Analysis Time series analysis is a very important tool in the data analysis which explains the variability of the different variables along with time factor. For this study, we can use the time series analysis for the data related to the brand of chips, availability, price, quantity. By using different time series techniques like moving average, exponential smoothing we can find out the estimates for the future. These estimates would be helpful while management policy of the industry. References Schervish, Mark J. (1995). Theory of statistics (Corr. 2nd print. ed.). New York: Springer Moses, Lincoln E. (1986) Think and Explain with Statistics, Addison-Wesley Hays, William Lee, (1973) Statistics for the Social Sciences, Holt, Rinehart and Winston Rubin, Donald B.; Little, Roderick J. A., Statistical analysis with missing data, New York: Wiley 2002 Mosteller, F., & Tukey, J. W. (1977). Data analysis and regression. Boston: Addison-Wesley. Mann, Prem S. (1995). Introductory Statistics (2nd ed.). Wiley. Babbie, Earl R. (2009). The Practice of Social Research (12th ed.). Wadsworth. Nick, Todd G. (2007). "Descriptive Statistics". Topics in Biostatistics.New York: Springer. Trochim, William M. K. (2006). "Descriptive statistics". Research Methods Knowledge Base. Prepare an 11- to 15-slide Microsoft PowerPoint presentation for the senior management team based on the business problem or opportunity you described in Week 3 and 4. Draw on material you developed in the Week 3 and 4 assignments. Include the following in your presentation: Introduction slide Agenda slide Describe the organization, with a brief description Explain the business problem or opportunity Analyze why the business problem is important Identify what variable would be best to measure for this problem. Explain why. Apply data analysis techniques to this problem (tell which techniques should be used: descriptive stats, inferential stats, probability, linear regression, time series). Explain why. Apply a possible solution to the problem/opportunity, with rationale. Evaluate how data could be used to measure the implementation of such a solution. Conclusion References slide (if any source material is quoted or paraphrased throughout the presentation) Include on the slides what you'd want the audience to see (include appropriate visual aids/layout) and include in the Speaker's Notes section what you'd say as you present each slide. If any source material is quoted or paraphrased in the presentation, use APA citations and references. Click the Assignment Files tab to submit your presentation