Chi-Square Analyst At A Local Bank Wonder

Hw11mgmt 07312021chi Squarean Analyst At A Local Bank Wonders If T

HW11 MGMT 07/31/2021 Chi Square An analyst at a local bank wonders if the age distribution of customers coming for service at his branch in town is the same as at a branch located near the mall. He selects 100 transactions at random from each branch and researches the age information for the associated customer. These are the data: Age less than or older Total In town mall Total What is the null hypothesis if you want to check if the age patterns of customers are independent of bank location? What are the expected numbers for each cell in a 3 by 3 table if the null hypothesis is true? Use the chi square test to accept or reject the null hypothesis. What is the chi square test statistic? What is the chi square critical value and how many degrees of freedom does it have? Assume alpha is 0.05. What do you conclude? ANOVA Saeko owns a yarn shop and wants to expand her color selection. Before she expands her colors, she wants to find out if her customers prefer one brand over another brand. Specifically, she is interested in three different types of bison yarn. As an experiment, she randomly selected 21 different days and recorded the sales of each brand. At the 0.10 significance level, can she conclude that there is a difference in preference between the brands? Misa's Bison Yak-et-ty-Yaks Buffalo Yarns Total 4,735.00 4,603.00 5,012.00 What is the null hypothesis? What is the alternative hypothesis? What is the level of significance? Use Tools - Data Analysis - ANOVA: Single Factor to find the F statistic: From the ANOVA output: What is the F value? What is the F critical value? What is your decision? Explain in statistical terms Regression studies have shown that the frequency with which shoppers browse Internet retailers is related to the frequency with which they actually purchase products and/or services online. The following data show respondents' age and answer to the question “How many minutes do you browse online retailers per year?” Use Data > Data Analysis > Correlation to compute the correlation checking the Labels checkbox. Use the Excel function =CORREL to compute the correlation. If answers for #1 and 2 do not agree, there is an error. The strength of the correlation motivates further examination. a) Insert Scatter (X, Y) plot linked to the data on this sheet with Age on the horizontal (X) axis. b) Add to your chart: the chart name, vertical axis label, and horizontal axis label. c) Complete the chart by adding Trendline and checking boxes. Read directly from the chart: a) Intercept = b) Slope = c) R2 = Perform Data > Data Analysis > Regression. Highlight the Y-intercept with yellow. Highlight the X variable in blue. Highlight the R Square in orange. Use Excel to predict the number of minutes spent by a 22-year-old shopper. Enter = followed by the regression formula. Enter the intercept and slope into the formula by clicking on the cells in the regression output with the results. Is it appropriate to use this data to predict the amount of time that a 9-year-old will be on the Internet? If yes, what is the amount of time, if no, why? Cleaning Data with Outlier On this worksheet, make an XY scatter plot linked to the following data: X Y 1.01 2.48 4.8 4.81 5.07 2.53 3.46 4.38 3.77 4.88 4.32 3.75 3.94 5.19 2.56 4.16 2.22 2.72 5.45 4.43 4.19 3.6 3.58 3. Add trendline, regression equation, and R-squared to the plot. Add this title: ("Scatterplot of X and Y Data"). The scatterplot reveals a point outside the point pattern. Copy the data to a new location in the worksheet. You now have 2 sets of data. Data that are more than 1.5 IQR below Q1 or more than 1.5 IQR above Q3 are considered outliers and must be investigated. It was determined that the outlying point resulted from data entry error. Remove the outlier in the copy of the data. Make a new scatterplot linked to the cleaned data without the outlier, and add title ("Scatterplot without Outlier,"), trendline, and regression equation label. Compare the regression equations of the two plots. How did removal of the outlier affect the slope and R2? Explain why the slope and R Square change the way they did. Observing Student Engagement in Science and Health for 2nd Grade (56:09 minutes) (31:53 minutes) (48:06 minutes) (31:43 minutes) minutes) Science Pre-Assessment Videos (21:59 minutes) (30:08 minutes) (39:22 minutes) (31:45 minutes) (56:09 minutes) (25:30 minutes) Clinical Field Experience A: Science Observation Form Part 1: Observation Grade Level: Description of Science Lesson: Describe additional academic content areas that were present in the lesson. Document the evidence that demonstrates that the teacher encouraged students to understand the material and ask questions for clarity. Document opportunities that reflect on prior content knowledge, including cultural relevance, and specific academic language to support the instruction in the identified science content area. Document how students responded during instruction and independent work. Describe how students were assessed throughout and at the end of the lesson. Did all students participate? Part 2: Reflection © 2018. Grand Canyon University. All Rights Reserved. Clinical Field Experience B: Science and Health Pre-Assessment Part 1: Pre-Assessment and Implementation Grade level of mentor class: Standards being taught in mentor class: Description of unit being taught in mentor class: word description of Pre-assessment: Feedback from mentor teacher: Part 2: Reflection © 2018 Grand Canyon University. All Rights Reserved.

Paper For Above instruction

This comprehensive analysis explores multiple statistical methodologies and research inquiries, beginning with a chi-square test to examine whether customer age distributions at two different bank branches are independent of location. The null hypothesis posits that age patterns are independent of branch location, indicating no association between customer age distribution and bank branch. To evaluate this, a 3x3 contingency table is constructed, and expected cell counts are calculated based on the marginal totals under the null hypothesis. The chi-square test statistic is then computed by summing squared differences between observed and expected frequencies, divided by the expected frequencies across all cells. Utilizing an alpha level of 0.05 with degrees of freedom calculated as (rows - 1)*(columns - 1) = 4, the critical value from chi-square distribution tables is determined. The comparison of the test statistic with this critical value leads to a conclusion: either failing to reject or rejecting the null hypothesis, thereby indicating whether age distribution differs by location.

Transitioning to the analysis of customer brand preferences for yarn, the null hypothesis states that there is no difference in preference among the three yarn brands. The alternative hypothesis suggests that at least one brand differs in preference. The significance level is set at 0.10. An ANOVA test is used to compare the means of sales across the different brands, utilizing Excel’s Data Analysis tools to compute the F statistic. The resulting F value and its critical value — determined at the same significance level — guide the decision. A conclusion about whether preferences differ significantly can be made based on whether the F statistic exceeds the critical value.

Furthermore, the research encompasses regression analysis examining the relationship between age and online browsing time. Correlation coefficients, obtained through Excel’s Data Analysis and the =CORREL function, measure the strength and direction of association. Scatter plots with trendlines visually demonstrate the relationship, with regression equations presenting intercepts and slopes indicating the expected browsing time for specific ages. R-squared values quantify the proportion of variance explained by the model, and predictions are made for a 22-year-old shopper’s browsing minutes. The appropriateness of predictions for younger ages, such as 9-year-olds, is critically evaluated considering the assumption of linearity and the range of the data.

In data cleaning, outliers are identified via IQR analysis within scatterplots. The process involves copying data, computing Q1, Q3, and IQR, and removing data points flagged as outliers due to data entry errors. Re-plotting the cleaned data reveals how the regression equation, slope, and R-squared value are affected by the removal of outliers, illustrating their influence on model accuracy and reliability.

Finally, the observational and formative assessment components highlight the importance of detailed documentation of science lessons to understand student engagement and instructional strategies. Reflective practices emphasize describing content, student responses, assessment, and cultural relevance within teaching units. Overall, this collection of statistical tools and educational insights demonstrates the application of quantitative and qualitative methods to inform research, teaching practices, and data-driven decision making in educational contexts.

References

• Agresti, A. (2018). An Introduction to Categorical Data Analysis. Wiley.
• Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. Sage.
• Gregory, R. (2018). Statapedia: Essentials of Statistics. Routledge.
• Ott, R. L., & Longnecker, M. (2015). An Introduction to Statistical Methods and Data Analysis. Cengage Learning.
• Sheskin, D. J. (2011). Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press.
• Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics. Pearson.
• Yule, G. U. (2016). Reports of the Royal Statistical Society. Wiley.
• Zuur, A. F., Ieno, E. N., & Smith, G. M. (2007). Analysing Ecological Data. Springer.
• Moore, D. S., & McCabe, G. P. (2017). Introduction to the Practice of Statistics. W. H. Freeman.
• De Veaux, R. D., Velleman, P. F., & Bock, D. E. (2016). Stats: Data and Models. Pearson.