Student Name: ____Chesey Colston ______ Date: _____12/3/2014_____ Class: Algebra 2 Explore Linear Models: Years of School Average Weekly Paycheck Some high school 11 450 High school graduate 13 585 Some college 15 810 Bachelor’s degree 17 1000 Master’s degree 19 1250 Doctoral degree 21 1600 1) Which type of function (linear, exponential, or cubic) do you believe will best fit the data? Support your choice. My choice would be a Linear Function because it doesn’t go through an intersection like an exponential function would, it also wouldn’t be a cubic function because it is not curved. It has all straight lines. 2) Find the rate of change between the data points of these intervals: (High school graduate) and (Some college) High …show more content…
Describe the pattern you see in them. 4) Use exponential regression (or find a constant ratio) to determine an equation for the data. 5) Graph the equation you wrote in step four superimposed over the original data. Comment on how well or how poorly the equation fits the data. 6) If the rounds of forwarding continued in the same pattern, predict the round that would be first to end up sending the link to more than 50,000 people. Explain how you came up with this prediction. Explore Cubic Models: Record the volumes. Size of Corner Cut Volume of the Box (lwh) 1 100 2 3 4 5 Graph the table of data. Use your graph to answer the questions that follow. 1) Which type of function (linear, exponential, or cubic) do you believe will best fit the data? Support your choice. 2) Find the rate of change between the data points of these intervals: (Corner Cut 1) and (Corner Cut 2) (Corner Cut 2) and (Corner Cut 3) (Corner Cut 3) and (Corner Cut 4) 3) What do the rate of change values you just calculated represent? Why are some positive and some negative? 4) Use cubic regression to determine an equation for the data (or lwh where (12 – x) represents the sides and (x) represents the height of the box). 5) Graph the equation you wrote in step four superimposed over the original data. Comment on how well or how
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1. Use the graph below to predict what the results will look like if the null hypothesis is
5. Restate your predictions that were correct and give the data from your experiment that supports them. Restate your predictions that were not correct and correct them, giving the data from your experiment that supports the correction.
Using the equations you wrote in problems 4-7, create an Excel spreadsheet with the columns “5-minute interval,” “Number of people told,” and “Number of people who know”. Make sure your spreadsheet matches your tables above before answering the following questions.
Introduction: Today scientists put acquired data into a form of a graph. This said graph is designed to help make predictions and furthermore, study and understand the experiment and its contents at hand. The Graphing and Estimating lab involves just that. The lab is designed to collect data from several tests involving burn time of a candle.
Present all relevant data in a data table below. Include an observations section for any observations you made during the lab. Make sure you note the data needs to be converted before graphing.
7. Graph the data from Table 1: Water Quality vs. Fish Population (found at the beginning of this exercise). You may use Excel, then “Insert” the graph, or use another drawing program. You may also draw it neatly by hand and scan your drawing. If you choose this option, you must insert the scanned jpg image here.
For the first five data point, the value of exponential model is close to the actual value. However, the exponential model didn’t work well for the
29. A distribution center for a chain of electronics supply stores fills and ships orders to retail outlets. A random sample of orders is selected as they are received and the dollar amount of the order (in thousands of dollars) is recorded, and then the time (in hours) required to fill the order and have it ready for shipping is determined. A scatterplot showing the times as the response variable and the dollar amounts (in thousands of dollars) as the predictor shows a linear trend. The least squares regression line is determined to be: yˆ= 0.76 +1.8x. A plot of the residuals versus the dollar amounts showed no pattern, and the following values were reported: Correlation r +0.90; R 2 = 0.81; standard deviation of the residuals is 0.48. What percentage of the variation in the times required to prepare an order for shipping is accounted for by the fitted line?
The scatter plot will allow us to compare how the variables arrange themselves on the graph and how the linear, quadratic, and power functions correspond to the data on the graph.
However, in order to find a linear slope of a parabolic curve, the mathematical equation of distance/time^2 ( pertaining to the data as cm/s^2) is used to produce the linearization of the distance-time graph. A linear distance-time graph is the equivalent of a velocity-time graph by identifying the slope of the distance-time graph. Another best fit line (a linear trendline) was plotted within the newly charted velocity-time graph. The equation identified through the trendline was Velocity=808.578cm/s^2 (t) +44.450 cm/s. The 44.450cm/s represents the point in which the linear data crosses the y-axis as the y-intercept. Additionally, the 808.578cm/s^2 represents the slope of the velocity-time graph in which the acceleration can be