ContextCritical flicker frequency (CFF) and eye colorComputer screens and fluorescent bulbs flicker. If the frequency of the flicker is below a certain threshold, the eye detects the flicker, and it is annoying!Different people have different flicker "threshold" frequencies (known as the critical flicker frequency, or CFF). The mean critical threshold frequency is important for product manufacturing as well as tests for ocular disease.In 1973, researchers conducted a study to answer the following question.Research question: Do people with different eye color have different threshold flicker sensitivity?The 1973 study ("The Effect of Iris Color on Critical Flicker Frequency," Journal of General Psychology [1973], 91–95) obtained the following data from a random sample of 19 subjects.ColorThresholdFrequency (CFF)BlueBrownGreen Brown 26.825.726.826.4 Brown 27.927.227.924.2 Brown 23.729.923.728 Brown 25.028.52526.9 Brown 26.329.426.329.1 Brown 24.828.324.8 Brown 25.725.7 Brown 24.524.5 Green 26.4 Green 24.2 Green 28.0 Green 26.9 Green 29.1 Blue 25.7 Blue 27.2 Blue 29.9 Blue 28.5 Blue 29.4 Blue 28.3In this spreadsheet the data is presented in two formats.Stacked data: The quantitative data is stacked in one column. The first two columns show the data in a stacked format. Each variable is a column (one column for the explanatory variable eye color; one column for the response variable CFF) and each row is an individual. For example, the first row is a brown-eyed person with a CFF of 26.8.Unstacked data: The quantitative data is distributed across the groups in multiple columns. The last three columns show the same data in an unstacked format. In this format, each column is a group defined by a value of the explanatory variable: one column for blue-eyed people, one column for brown-eyed people and one column for green-eyed people. Each column contains the response values (CFF) for that group.The format of the data in the spreadsheet affects how we use StatCrunch to analyze it.VariablesColor: This is the explanatory variable. The categorical data represents the groups we will compare.CFF (flicker threshold sensitivity): This is the response variable. The quantitative data represents the frequency threshold at which the subject sees the flicker.Datahttps://www.statcrunch.com/app/?dataid=3748367&tok... use this link for the data!!!!!PromptWe will conduct an ANOVA F-test for the variables Color and CFF. The flicker datafile is available in the Data section below. Also, the StatCrunch directions are provided in the list a the bottom of this page.What are the hypotheses for the ANOVA test? Be sure that you define clearly the parameters.Are the conditions that allow us to safely use the ANOVA F-test met? Explain.Note: To verify conditions, you will need to examine the distribution of CFF scores for each sample (because the samples are small).Use StatCrunch to create side-by-side dotplots, histograms or boxplots (your choice) to examine the distribution of CFF scores for each sample. You can use either data format; choose one (stacked data in the first two columns; or unstacked data in the the last three columns). To create the side-by-side graphs (for either data format) see the list of StatCrunch directions below. Download the StatCrunch output window (your graph), upload it to your Stats-Class folder, and then embed the .png file (your graph) in your initial post. To recall how to complete these tasks, see the list of StatCrunch directions below.You will also need to compare the sample standard deviations. Use StatCrunch to find the summary statistics, means and standard deviations for the comparison groups (select the the appropriate Descriptive Statistics StatCrunch directions from the list below). Then copy and paste the table into your initial post and explain how the rule of thumb for comparing standard deviations is met.Use StatCrunch to carry out the ANOVA F-test (select the appropriate ANOVA StatCrunch directions from the following two options). Anova F-test Stacked Data Format ORAnova F-test Unstacked Data FormatCopy and paste the output table into your initial post.State your conclusion in context of eye color and mean CFF.

Assignment ContentPurpose  This assignment is intended to give you an opportunity to strengthen your skills in gathering and analyzing business-related information. It provides a deeper understanding of how companies can look at globalization as part of their strategic and operational plans. The assignment has two parts: one focused on information research and analysis, and the other is on applied analytics. Resources: Microsoft Excel® "How Netflix Expanded to 190 Countries in 7 Years" from Harvard Business ReviewCallCenterWaitingTime.xlsx file Part 1: Globalization and Information Research Context: Companies that perform well in their country of origin usually consider expanding operations in new international markets. Deciding where, how, and when to expand is not an easy task, though. Many issues need to be considered before crafting an expansion strategy and investing significant resources to this end, including: the level of demand to be expected for the company’s products/services presence of local competitors the regulatory, economic, demographic, and political environments Carefully researching and analyzing these and other factors can help mitigate the inherent risk associated with an overseas expansion strategy, thus increasing the likelihood of success. As a data analyst in your company’s business development department, you’ve been tasked with the responsibility of recommending countries for international expansion. You’ll write a report to the company’s executive team with your research, analysis, and recommendations. Instructions: Write a 525-word summary covering the following items: According to the article listed above, what were the most important strategic moves that propelled Netflix’s successful international expansion? The article mentions investments in big data and analytics as one of the elements accompanying the second phase of overseas expansion. Why was this investment important? What type of information did Netflix derive from the data collected? According to the article, what is exponential globalization? Not all international expansion strategies are a resounding success, however. Research an article or video that discusses an instance in which an American company’s expansion efforts in another country failed. According to the article/video you selected, what were the main reasons for this failure? Do you agree with this assessment? Explain some of the reasons why certain companies’ expansion plans have failed in the past. Part 2: Hypothesis testing Context: Your organization is evaluating the quality of its call center operations. One of the most important metrics in a call center is Time in Queue (TiQ), which is the time a customer has to wait before he/she is serviced by a Customer Service Representative (CSR). If a customer has to wait for too long, he/she is more likely to get discouraged and hang up. Furthermore, customers who have to wait too long in the queue typically report a negative overall experience with the call. You’ve conducted an exhaustive literature review and found that the average TiQ in your industry is 2.5 minutes (150 seconds). Another important metric is Service Time (ST), also known as Handle Time, which is the time a CSR spends servicing the customer. CSR’s with more experience and deeper knowledge tend to resolve customer calls faster. Companies can improve average ST by providing more training to their CSR’s or even by channeling calls according to area of expertise. Last month your company had an average ST of approximately 3.5 minutes (210 seconds). In an effort to improve this metric, the company has implemented a new protocol that channels calls to CSR’s based on area of expertise. The new protocol (PE) is being tested side-by-side with the traditional (PT) protocol. Instructions: Access the CallCenterWaitingTime.xlsx file. Each row in the database corresponds to a different call. The column variables are as follows: ProtocolType: indicates protocol type, either PT or PEQueueTime: Time in Queue, in secondsServiceTime: Service Time, in secondsPerform a test of hypothesis to determine whether the average TiQ is lower than the industry standard of 2.5 minutes (150 seconds). Use a significance level of α=0.05. Evaluate if the company should allocate more resources to improve its average TiQ. Perform a test of hypothesis to determine whether the average ST with service protocol PE is lower than with the PT protocol. Use a significance level of α=0.05. Assess if the new protocol served its purpose. (Hint: this should be a test of means for 2 independent groups.) Submit your calculations and a 175-word summary of your conclusions.