Objective: Analyze the temporal dynamics between Income and HealthScore to identify trends, seasonal patterns, and potential predictive relationships. Assumption: Assume that the dataset includes multiple records per participant over different time periods (e.g., monthly, quarterly), with an additional variable: • TimePeriod: Indicates the time point of the record (e.g., January 2020, February 2020, ..., December 2023). Tasks: 1. Data Structuring: Restructure the statsnew.csv dataset to create time series objects for each participant, capturing Income and HealthScore across TimePeriod S. • Handle any missing time points or irregular intervals. 2. Exploratory Time Series Analysis: • . Plot the time series for Income and HealthScore for a representative sample of participants. Decompose the time series to identify trend, seasonal, and residual components using STL decomposition. 3. Cross-Correlation Analysis: Compute the cross-correlation function (CCF) between Income and HealthScore to identify any leading or lagging relationships. . Interpret the significance of the correlations at different lags. 4. Predictive Modeling: • Develop Vector Autoregression (VAR) models to capture the interdependencies between Income and HealthScore over time. . Determine the appropriate order of the VAR model using criteria like AIC or BIC. • Forecast future HealthScore based on projected Income and vice versa. 5. Causality Testing: Perform Granger Causality tests to examine whether Income can predict HealthScore and/or if HealthScore can predict Income.

The Seneca Children’s Fund (SCF) is a local charity that runs a summer camp for disadvantaged children. The fund’s board of directors has been working very hard over  recent years to decrease the amount of overhead expenses, a major factor in how charities are rated by independent agencies. The following data show the percentage of the money SCF has raised that was spent on administrative and fund-raising expenses over the past seven years:   Year Period (t) Expense (%) 2003 1 13.9 2004 2 12.2 2005 3 10.5 2006 4 10.4 2007 5 11.5 2008 6 10.0 2009 7 8.5   a. Construct a time series plot. What type of pattern exists in the data?b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.c. Forecast the percentage of administrative expenses for year 8.d. If SCF can maintain its current trend in reducing administrative expenses, how long will it take for SCF to achieve a level of 5% or less?

Cryptocurrencies have rapidly become an important alternative to traditional currencies for many types of transactions. Etherium, one of the most prominent cryptocurrencies, has rapidly appreciated in value. Daily Etherium trading information for the first 332 days of 2021.  It includes the following variables: Date Day – Day of the year, used to assess trend over time Volume (US $) – Daily trading volume Opening Price (US $) – Opening price for daily trading Price Change (US $) – Daily change in price from opening to close 1. Determine the sample correlation coefficient, r, between Volume and Price Change. Test the alternative hypothesis that Volume has a linear relationship to Price Change. Specifically, what are the test statistic and the p-value for that test statistic? For α = .05, what do you conclude about the relationship between the variables? (reminder: the T.DIST.2T function requires input of a positive test statistic)

What do the Forecast for each year add up to?

The table and graph below both display the percentage of people in the United States who do not have health insurance over several different years. Data downloaded on 2/19/2020 from https://ourworldindata.org/health-meta. 30.0% 25.0% € 20.0% 15.0% 10.0% 5.0% Year 0.0% Part 1 24.3% 14.5% 1960 1970 1980 1963 1968 1994 2012 Percent without Health Insurance 24.3% 14.5% 15.8% 14.7% Part 2 Use the table to answer the questions in each part below. 15.8% Part 3 Which variable is the independent variable and which is the dependent variable? Choose the correct response below. You only get one submission. T 14.7% 1990 2000 2010 2020 Year O Percent of people without health insurance is the independent variable and year is the dependent variable. O Year is the independent variable and percent of people without health insurance is the dependent variable. Average Rate of Change In what unit is the average rate of change in the percentage of people without health insurance measured? You get two…

Only data that shows positive trends can be graphed in a scatter diagram. True O False

The creator of a new online multiplayer survival game has been tracking the monthly downloads of the game. The following table shows the monthly downloads (in thousands) for each month of the current and previous year. Click on the datafile logo to reference the data.   The datafile logo.   Month Downloads Month Downloads (previous year) (thousands) (current year) (thousands) February 33.0 January 31.0 March 34.0 February 32.0 April 34.0 March 33.0 May 31.0 April 34.0 June 32.0 May 33.0 July 33.0 June 32.0 August 35.0 July 35.0 September 36.0 August 33.0 October 37.0 September 33.0 November 34.0 October 31.0 December 35.0     a. Compute the mean, median, and mode for number of downloads in the previous year. Mean (to 3  decimals)   thousand Median (to 2 decimal)   thousand Mode (to 2 decimal)   thousand b. Compute the mean, median, and mode for number of downloads in the current year. Mean (to  3 decimals)   thousand Median (to 2…

An electronic appliance manufacturer wants to know if there is a relationship between percentage change in deposable personal income which is reported quarterly by the government, and the percentage change in appliances sold by the manufacturer following same years of quarterly data. Brenda Chee and Clarence Paulus lead an analyst team has obtained data for the past 10 quarters. (Hint: Provides your answers in two decimal points) Quarter Percent change in income Percent Change in appliance sold Quarter Percent change in income Percent change in appliance sold 1 -2.3 -2.5 6 -1.0 1.0 2 -1.5 -1.0 7 0.7 1.4 3 2.8 7.4 8 5.2 3.4 4 0.5 2.6 9 -2.5 -0.5 5 4.6 8.5 10 1.7 1.8 (a) What forecasting model should be used for this data. Why?(5(b) Develop the forecasting model that you have proposed in (a).(c) Compute the relationship for these data. In your opinion, is the relationship between independentvariable…

The creator of a new online multiplayer survival game has been tracking the monthly downloads of the game. The following table shows the monthly downloads (in thousands) for each month of the current and previous year.  Click on the datafile logo to reference the data.    Month  Downloads  Month  Downloads  (previous year)  (thousands)  (current year)  (thousands)  February  33.0  January  37.0  March  34.0  February  35.0  April  34.0  March  37.0  May  32.0  April  38.0  June  32.0  May  35.0  July  35.0  June  36.0  August  35.0  July  35.0  September  37.0  August  35.0  October  37.0  September  34.0  November  36.0  October  31.0  December  34.0      Compute the mean, median, and mode for number of downloads in the previous year. Mean (to 3 decimals)   thousand  Median (to 2 decimal)   thousand  Mode (to 2 decimal)    thousand…

The merchants in Dallas, Texas, suffered flood damage in May 2003. Stores were closed for remodeling nearly two months. What is this type of variation in sales called?   Multiple Choice   A trend   Seasonality   A cycle   An irreguular component