What Is The Variable

| Use this information for questions that refer to the World Tennis Ball (WTB) Company case.

We have data out of 250 stores. The data include demographics, economics, sales of the stores, compositions of those sales as well as sales behavior per households. There are 31 variables being consider for each store and those variables range from sales,

PowerPoint Presentation must be 10 slides and graphically display the statistical data developed for three comparable cities, counties, or states. Your presentation must incorporate national statistics for comparison. Your assessment may be in bullet or paragraph format and will be provided in the notes section of the presentation.

If temperature varies from 16 to 25 degrees each day, the standard deviation of temperature will be greater than if temperature only varies from 20 to 22 degrees each day. The standard deviation can be calculated using Excel (we will see how in class). You will want to make graphs and/or tables of these relationships for your report. Here is an example of the type of graph you will need to make. By convention, the independent variable (the “cause”) is plotted on the x-axis, while the dependent variable (the “effect” or consequence) is plotted on the y-axis. In the example below, which is the independent variable and which is the dependent variable? Age (years) 2 3 4 5 6 7 8 9 10 11 12 13 Height (cm) 91 99 107 112 117 124 130 135 140 145 150 155

Answer: Age in years is a discrete variable. If a person is 10 years old or considers themselves 10.5, in data age generally goes by a whole number.

Fig. 4 shows that the fit of this sum to the original data is robust. The annual and semi-annual frequencies are seen to reproduce or account for most of the pattern in the hospital admission data, indicating that asthma is very seasonal in El Paso. The fall peak occurs at the end of the rainy season; the spring during the severe dry season winds. Note amplitude and phase shifts associated with the 2006 July-August monsoon. The fall asthma peak occurred a month early, with extreme intensity.

The variables regarding the quarterback were averaged over the number of games the quarterback started. In contrast, the remaining two variables (defensive YPG and running YPG) were averaged over the full 16-game season. My dependent variable was the win percentage for however many games the quarterback played. My data had 201 observations, going back 5 years (from the 2010 season to the 2015 season).

Physical factors that can contribute to the development of the flu are transportation and living conditions. Often times people have no access to healthcare because of transportation. If one does not have a care, they must rely on public transportation. Depending on the availability of healthcare, this may not be an option for some people. Living conditions plays a role in the development of the flu as well. In poorer communities, many people live in public housing. These living conditions are by far not the best. Buildings can be run down, unkept, and crowded. They many not have functioning heat or air conditioning. Once a disease like the flu develops, it can spread like wild fire through these types of communities.

The first variable considered is Location, a categorical variable. The three subcategories are Urban, Suburban and Rural. The frequency distribution and pie chart are included. Measures of central tendency and descriptive statistics are not calculated due to the categorical nature of the variable.

The mean time an American family lives in a particular single-family dwelling is 11.8 years. A sample of 100 families in the Sycamore, OK area shows the mean time living in a single family residence is 12.7 years. Can we conclude that the time people live in Sycamore is significantly more than the national average? One Sample Z-Test

The average (mean) number of years lived in the current home is less than 13 years

According to the Centers for Disease Control and Prevention (CDC, 2013) national healthcare disparities report for 2010, influenza and pneumonia are ranked within the top ten major causes of death in the United States. The influenza viruses that will be discussed throughout this case study include the human influenza viruses: A and B. These viruses are responsible for seasonal epidemics during the fall and winter around the globe in temperate environments. Historically influenza outbreaks have had a seasonal distribution and characteristic time course although according to the CDC (2017) factors influencing the extent and severity of an outbreak are less clear. Generally, there are two or three different influenza strains typically circulating concurrently in any given

The variable age is the independent variable and is a ratio level of measurement (Loiselle et al., 2011). The measure of central tendency to describe age are in table 1.2 are the mean of 57.62 which is the average age, the median which is the middle score within the distribution when all scores are organized of 58.5 and the mode of 58 which is the most frequently occurring age (Loiselle et al., 2011). The measures of variability are the range of 69 with a minimum age of 22 and a maximum age of 91, standard deviation which is the average deviation from the sample mean which is a value of 16.26, and the sample variance which is the standard deviation square and the value is 263.46 (Salkind, 2013). The distribution for this sample is described as a negative skew and the value obtained from table 1.2 is -0.22511(Salkind, 2013). A negative skew occurs when the median and the mode value are larger than the mean, within this sample the median is 58.5 the mode is 58 which is greater than the mean of 57.62, the tail would be pointed toward the left (Salkind, 2013). The kurtosis value is -0.65102 and this describes how peak or flat the curve is from the normal distribution curve which is described as mesokurtic (Salkind, 2013). The kurtosis has a large negative value which is representative of a flatter curve also know as playkurtic (Salkind, 2013).

These represent the range of the sale price. Lastly, I used the formula to get the standard deviation 48,945.28, which measures the variability.

Since the NHS data has been shown to have trend and seasonality we will evaluate the data using four different time series models and compare the results of each to see which model is the most accurate. The models we are going to use are the Modified Naïve model, Winters Exponential Smoothing model, Time Series Decomposition, and Autoregressive Integrated Moving Average (ARIMA).