**Instruction for Creating a Scatter Plot with Linear Regression**1. **Objective**: Use technology (Desmos or a graphing calculator) to create a scatter plot of your data, incorporating the linear regression line.2. **Instructions**: - If using Desmos: - Include a screenshot that clearly displays the scatter plot points, regression line, and axes. - Ensure each axis is labeled with the appropriate variable and word label. - If not using Desmos, sketch the graph as accurately as possible.3. **Additional Tips**: - Consider taking a screenshot of your graph and pasting it into a digital document like OneNote or MS Word for submission. - Utilize resources like DESMOS or Microsoft Excel to create scatter plots and regression lines.4. **Resource Link**: - Access the Desmos calculator here: [Desmos Calculator](https://www.desmos.com/calculator/gautfghyvh).5. **Important Note**: - Ensure all data points are accounted for before answering subsequent questions.This guidance will aid in visualizing and analyzing data through scatter plots and regression, enhancing your understanding and interpretation of data relationships. ### Exploring Linear Relationships: Systolic Blood Pressure and AgeIn many scientific studies, identifying relationships between variables is crucial for understanding patterns and making predictions. A common statistical method used is to establish a **linear regression line** to predict one variable based on another or to determine the value of the **correlation coefficient** to measure the strength of their relationship.Here, we explore the relationship between **age** (independent variable, $x$) and **systolic blood pressure** (dependent variable, $y$), using the following dataset:**Dataset: Systolic Blood Pressure**- **Independent ($x$) variable**: Age (years)- **Dependent ($y$) variable**: Systolic Blood Pressure (mmHg)| Age | Systolic Blood Pressure ||-----|-------------------------|| 18 | 114 || 19 | 124 || 20 | 116 || 21 | 120 || 25 | 125 || 30 | 130 || 32 | 110 || 36 | 136 || 39 | 144 || 42 | 124 || 44 | 160 || 45 | 135 || 46 | 142 || 47 | 145 || 48 | 220 |To analyze the data, you can use tools like Desmos.com or graphing calculators. It’s helpful to draw lines below each row and count each row to keep track of the number of data points, which are organized as ordered pairs.This data can be used to establish a linear regression model, conduct further statistical analyses, and understand how systolic blood pressure might change with age.

Introduction - Regression line equation y=ax+b where , a=y-intercept b=slope a=(∑y)(∑x2)-(∑x)…

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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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The table shows the approximate chirping rate at various temperatures for one type of cricket. Use the linear regression feature on a graphing calculator to determine an equation of the line that best fits the data. Round to the hundredths. ⒸEncikAn/Shutterstock.com X Temperature (°F) 50 60 70 80 100 Chirps per Minute 20 80 115 150 250
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The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. 220- 210 200 190 180 170 160 150 Year 0 130- 120- 110- 100- 90 801 6 12 15 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. Retail Sales 85.851 108.426 141.781 169.256 202.297 222.266 3 6 9 12 15 Use the above scatter plot to decide whether the linear model fits the data well. The function is a good model for the data. The function is not a good model for the data Estimate the retails sales in the U. S. in 2015. billions of dollars.
H. Find the slope and y-intercept of each regression line. Then, determine if the correlation between the variables is positive or negative. (12 points) Regression Line Equation 1. Y = 1.751X – 21.722 Slope y-intercept Kind of Correlation 2. Y = -221.943X+65.434 3. Y = -82.455X –314.986 4. Y = 0.997X + 0.168
Below is the output from regression. Use the p-value and decide if the equation is good to use for forecasting. Group of answer choices A.) p-value = 82 - equation is good to use B.) p-value = 0.15 - equation is not good to use C.) p-value = 0.18 - equation is not good to use D.) p-value = -16 - equation is good to use
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, x Stories, y (b) x = 649 feet (d) x = 724 feet 766 620 520 508 494 484 (a) x = 503 feet 51 46 44 43 39 38 (c) x = 318 feet Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. O A. O B. O C. O D. 60- 60- 60 60- 0- 800 800 800 Height (feet 800 Height (feet) Height (Teet) Stories Stories:
Find the equation of the line which best fits the data points (2;1) , (4;4), (5;1),(6;3).

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