The defending attorney Mr. Justin Case was interested in how a lengthy trial could affect how long a jury would deliberate on a case (and see if he should just cut to the chase). He observed a sample of courtroom trials and noticed the following: Days in Trial (X) 5 2 6 4 5 6 2 4 2 1  Hours Deliberation (Y) 4 4 1 3 1 3 9 2 3 7 2) What is the Y-intercept?

Can subpoints d to f be explained pleased

b) Alcohol is different in that is removed linearly from the body. The following table shows the blood alcohol level (BAL) a number of hours after they start drinking. The data given below is for an average 30 year-old woman (130 pounds, 5 feet 5) woman and for an average 30 year-old man (180 pound man, 5 feet 10) who have each drunk 4 large glasses of wine. Use Excel to graph BAL amount against time elapsed for (i) an average man, and (ii) an average man. Graph and paste the chart(s) into your assignment. Blood Alcohol Level (Woman) Blood Alcohol Hours Elapsed Level (Man) 0.129 0.107 1 0.110 0.091 0.076 0.060 2. 0.091 3. 0.073 4. 0.054 0.045 0.035 0.029 6. 0.017 0.014 c) The legal DUI limit in Arizona is 0.080%. Estimate the amount of time (in minutes) needed between the last drink and getting behind the wheel. Better still designate a driver.

You work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much a home's distance from the water affects its selling price. You are going to collect a random sample of 9 recently sold homes in your town. You will note the distance each hame is from the water (denoted by X, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also note the product X-y oaf the distance from the water and selling price for each hame. (These products are written in the row labeled "Xy")- (a) Click an "Take Sample" to see the results for your random sample. Distance from the water, X 3.8 21 1.3 0.2 29 21 4.4 1.3 1.8 (In lam) Selling price, y (In hundreds of thousands of dollars) Tako Sample 5.7 10.3 17.1 14.8 8.2 1.4 6.1 10.4 9.6 xy 21.66 21.63 22.23 2.96 23.78 15.54 26.84 13.52 17.28 Sand data to calculator v Based on the data from your sample, enter the indicated values…

Can you please answer 6 (a&b) and show how you did your work if you cannot the x and y-axis clear enough here: x: 4, 8,12,16,20,24,28,32,36,40,44,48 y: 100,200,300,400,500,600,700

Square Feet Number of Bedrooms Age Selling Price 2848 4 242100 1270 113600 1825 281700 2235 5 199100 2072 4 307500 2197 4. 14 278800 2184 4 275300 1764 7 107200 2276 14 103000 Copy Data N Prev Step 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.05 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant. E Tables E Keypad AnswerHow to enter your answer (opens in new window) Keyboard Shortcuts Selecting a checkbox will replace the entered answer value(s) with the checkbox value. If the checkbox is not selected, the entered answer is used. X O There is not enough evidence. X2 + © 2021 Hawkes Learning JIS A

Sometimes we have data that can be paired together, such as height and weight of an individual. When two variables are measured and paired together, we have what we call paired data. When we have paired data, we can plot the results using a scatterplot. This will give us a visual to determine if there is a relationship between the variables of interest.  For two variable relationships, we can often report the data as ordered pairs in (x, y) format. Our x-variable in this case is known as the explanatory variable, and the y-variable is known as the response variable. We use values of x to predict y.  If there appears to be a linear relationship between the two variables, we can create a least squares regression line. This is a line that “best” fits the dataset we have. The result will be a linear equation and we can use this equation to predict future values.   For simple linear regression (ordered pairs with only two variables), the general form of a regression line follows:�̂=β0+β1�,…

You work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much a home's distance from the water affects its selling price. You are going to collect a random sample of 7 recently sold homes in your town. You will note the distance each home is from the water (denoted by x, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also note the product x.y of the distance from the water and selling price for each home. (These products are written in the row labeled "xy"). (a) Click on "Take Sample" to see the results for your random sample. Distance from the water, .x (in km) Take Sample Selling price, y (in hundreds of thousands of dollars) xy Send data to calculator Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table…

Does the higher cost of tuition translate into higher-paying jobs? The table lists 10 colleges based on mid-career salary and the associated yearly tuition costs. (Comma separated lists of the data are also provided below the table to ease in copying the data to R.) Yearly Tuition (in thousands of dollars) (x) : 18, 25, 14, 25, 8, 12, 7, 13, 16, 23 y: 93, 125, 80, 129, 70, 82, 73, 128, 69, 143 1. Find the best fit line for the data. y = + $ x thousand 18 25 14 Question Help: D Post to forum Submit Question 25 8 12 7 13 16 23 2. Graph the scatter plot of the data together with the best fit line using R. (This portion of the problem is not graded.) 3. Predict the mid-career salary of a student attending a college with an annual tuition of $19 thousand. Mid-career Salary (in thousands of dollars) (y) 93 125 80 129 70 82 73 128 69 143

An business reviews data on the daily amount of calls it receives. Are the data discrete or continous?