Identify which of the following scatter plots contain possible outliers or observations influential for the least squares regression line. The word bank is at the bottom. Drag and drop all descriptions that apply ### Educational Website Content: Analysis of Statistical Graphs#### 1. Graph Analysis: Test Scores vs. Number of Incorrect Answers- **Graph Overview**: The scatter plot displays the relationship between test scores and the number of incorrect answers.- **Axes**: - **X-axis**: Number of incorrect answers (ranging from 0 to 20). - **Y-axis**: Test score (ranging from 40 to 100).- **Trend**: There is a negative correlation; as the number of incorrect answers increases, the test scores tend to decrease.#### 2. Graph Analysis: Years of Employment vs. Age- **Graph Overview**: This scatter plot illustrates the relationship between a person’s age and their years of full-time employment.- **Axes**: - **X-axis**: Age (ranging from 35 to 55 years). - **Y-axis**: Years of full-time employment (ranging from 0 to 25).- **Trend**: A positive correlation exists; older individuals tend to have more years of full-time employment.#### 3. Graph Analysis: Oil Changes vs. Distance Driven- **Graph Overview**: The scatter plot shows the relationship between the number of oil changes and the distance driven.- **Axes**: - **X-axis**: Distance driven (ranging from 20,000 to 120,000 miles). - **Y-axis**: Number of oil changes (ranging from 0 to 25).- **Trend**: A positive correlation is evident; as distance driven increases, the number of oil changes also tends to increase.#### 4. Graph Analysis: Daily Temperature Ranges- **Graph Overview**: This scatter plot represents the relationship between daily low and high temperatures.- **Axes**: - **X-axis**: Daily high temperature (ranging from 40°F to 55°F). - **Y-axis**: Daily low temperature (ranging from 25°F to 40°F).- **Trend**: A positive correlation exists; higher daily high temperatures tend to correspond with higher daily low temperatures.Each graph demonstrates a distinct type of correlation which can be vital for educational insights into data analysis and interpretation. This image contains two scatter plots analyzing different datasets, each with distinct variables and axes.### Left Scatter Plot:- **Title**: The relationship between the price of gasoline and the duration of commute.- **X-axis**: Price of gasoline in dollars per gallon (ranging from $2.00 to $2.30).- **Y-axis**: Duration of commute in minutes (ranging from 22 to 28 minutes).- **Data Points**: Each point represents a specific observation of gas price and the corresponding commute time.- **Observation**: The points show some variation, but no clear trend or pattern is immediately obvious.### Right Scatter Plot:- **Title**: The relationship between hours slept and coffee consumed.- **X-axis**: Hours slept (ranging from 5.5 to 8.5 hours).- **Y-axis**: Coffee consumed in ounces (ranging from 10 to 45 ounces).- **Data Points**: Each point represents an individual's hours slept and coffee consumption.- **Observation**: There appears to be a slight negative trend, where fewer hours of sleep might correlate with higher coffee consumption.### Answer Bank:Below the plots, there is an "Answer Bank" with the options:1. **No outliers or influential observations**2. **Influential observation**3. **Outlier**These options may relate to a question or analysis regarding the data points in each scatter plot and whether they include any statistical outliers or influential data.This visual data can be used to explore potential correlations between variables, though a deeper statistical analysis would be needed to draw definitive conclusions.

Scatter plots are visual representations of bivariate data. The X-axis represents one variable and…

Identify which of the following scatter plots contain possible outliers or observations influential for the least squares regression line. The word bank is at the bottom. Drag and drop all descriptions that apply

Identify which of the following scatter plots contain possible outliers or observations influential for the least squares regression line. The word bank is at the bottom. Drag and drop all descriptions that apply

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Listed. below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $115 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 41 21 114 68 79 51 118 65 9. 61 122 21 15 151 110 119 11 107 56 128 116 101 107 55 105 222 39 22 283 45 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y%= X. %3D (Round to one decimal place as needed.)
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
A. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. height 775 619 519 508 491 474 (a) x=503 feet (b) x=644 feet Stories, y 53 47 44 42 39 38 (c) x=798 feet (d) x=734 feet
Pls help ASAP
Paragraph Styles 1. Police investigators and forensic scientists can often predict the height of a suspect just from a footprint that was left behind at a crime scene. This is because the size of a person's foot tends to relate to the height of a person. As you might already know, this is a positive relationship. The taller the person, the larger their feet tends to be. Data given below: Length of footprint (cm) Height (cm) a) Determine the least squares regression line 29.8 29.3 27.5 31.1 32.6 22.5 176.1 179.4 172.2 184.2 179.0 157.3 b) Determine the linear correlation coefficient c) Create a scatter plot to display the data and draw the line of best fit.
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 442 pounds? Use a significance level of 0.05. Chest size (inches) 58 50 65 59 59 48 Weight (pounds) 414 312 499 450 456 260 Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? Critical Values of the Pearson Correlation Coefficient r y=D+x (Round to one decimal place as ne Critical Values of the Pearson Correlation Coefficient r NOTE: To test Ho: p=0 against H,: p+0, reject Ho if the absolute value of r is greater than the critical value in the table. a = 0.05 a = 0.01 4 0.950 0.990 0.878 0.811 0.754 5 0.959 6 0.917 7 0.875 8 0.707 0.834 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482…
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12