Explore math with our beautiful, free online graphing calculator. Graph functions, plot points, visualize algebraic equations, add sliders, animate graphs, and more.When to add a quadratic term? Start by fitting a linear regression model to the data ( Y = β0 + β1X Y = β 0 + β 1 X ), and plot the residuals versus the fitted values.Yao and Müller (2010) proposed a functional regression model with a quadratic term of the functional predictor. Suppose we have a functional predictor X ( t) and a scalar response Y; then the functional quadratic model is given by Y = α + ∫ X ( t) β ( t) d t + ∫ ∫ X ( s) X ( t) γ ( s, t) d s d t + ε, where α is an intercept; β ( t ...Jan 1, 2020 · The middle of the vertical axis is zero in all graphs. Functional regression models elucidate the complex relationship between repeatedly measured variables. In this paper, we constructed quadratic regression models for functional data where both the predictor and response are given as functions.The process is called quadratic regression and its goal is to minimize the least square error of the data with their corresponding points on the parabola. The calculator has a …Therefore, the quadratic model is either as accurate as, or more accurate than, the linear model for the same data. Recall that the stronger the correlation (i.e. the greater the accuracy of the model), the higher the R^2. So the R^2 for the quadratic model is greater than or equal to the R^2 for the linear model. Have a blessed, wonderful day! Desmos is a free online calculator that lets you create and explore graphs of quadratic regression and other functions. You can enter data points, adjust coefficients, and see …Step 3: Perform quadratic regression. Next, we will perform quadratic regression. Click on the Analyze tab, then Regression, then Linear: In the new window that pops up, drag happiness into the boxed labeled Dependent. Drag hours and hours2 into the box labeled Independent (s). Then click OK.The general form of the multiple quadratic polynomial regression model is as follows: where β 0 is a constant, and β j, β j,j, and β j,k are the linear, pure quadratic and interaction coefficients, respectively. Let . The multiple quadratic regression model can be represented as the following multiple linear regression model:Quadratic Regression Model Example. Data: The table at the right shows the horizontal distance (in feet) traveled by a baseball hit at various angles. The initial speed of the ball at the bat is constant. Batter up!!! Task: …In Part 3 we used the lm() command to perform least squares regressions. In Part 4 we will look at more advanced aspects of regression models and see what R ...Feb 24, 2012 · Here you can find the quadratic regression line [QUADREG], the cubic regression line [CUBICREG], and the exponential regression line [EXPREG]. Now you can look at each model and see which fits the best. Example 2. Determine whether the function in the given table is linear, quadratic or exponential. x y 0 5 1 10 3 20 4 25 6 35 Explore math with our beautiful, free online graphing calculator. Graph functions, plot points, visualize algebraic equations, add sliders, animate graphs, and more. A Tutorial, Part 4: Fitting a Quadratic Model - The Analysis Factor. R Is Not So Hard! A Tutorial, Part 4: Fitting a Quadratic Model. In Part 3 we used the lm () command to perform least squares regressions. In Part 4 we will look at more advanced aspects of regression models and see what R has to offer. One way of checking for non …http://mathispower4u.wordpress.com/The Gaussian Process Regression makes use of the Bayes’ theorem in the following, which is why it should be briefly explained in advance. In general, the Bayes’ theorem is defined as follows: It allows the inference from known values to unknown values. A often used application example is the disease detection. ... Rational Quadratic Kernel.Figure 1 – Data for polynomial regression in Example 1. We next create the table on the right in Figure 1 from this data, adding a second independent variable (MonSq) which is equal to the square of the month. We now run the Regression data analysis tool using the table on the right (quadratic model) in columns I, J and K as the input.Worked example. First we need to identify the values for a, b, and c (the coefficients). First step, make sure the equation is in the format from above, a x 2 + b x + c = 0 : is what makes it a quadratic). Therefore x = 3 or x = − 7 .One way to try to account for such a relationship is through a polynomial regression model. Such a model for a single predictor, X, is: Y = β 0 + β 1 X + β 2 X 2 + … + β h X h + ϵ, where h is called the degree of the polynomial. For lower degrees, the relationship has a specific name (i.e., h = 2 is called quadratic, h = 3 is called ... 6 Feb 2023 ... The general form of the multiple quadratic polynomial regression model is as follows: where β0 is a constant, and βj, βj,j, and βj,k are the ...Jun 2, 2012 · The secret to doing a quadratic or a cubic regression analysis is defining the Input X Range:. If you're doing a simple linear regression, all you need are 2 columns, X & Y. If you're doing a quadratic, you'll need X_1, X_2, & Y where X_1 is the x variable and X_2 is x^2 ; likewise, if you're doing a cubic, you'll need X_1, X_2, X_3, & Y where ... Therefore, the quadratic model is either as accurate as, or more accurate than, the linear model for the same data. Recall that the stronger the correlation (i.e. the greater the accuracy of the model), the higher the R^2. So the R^2 for the quadratic model is greater than or equal to the R^2 for the linear model. Have a blessed, wonderful day! 14 Dec 2023 ... It is clear that for each y value, there are two compatible X values, and I think the correct probability distribution for missing values is in ...Explore math with our beautiful, free online graphing calculator. Graph functions, plot points, visualize algebraic equations, add sliders, animate graphs, and more.Jan 11, 2022 · Quadratic functions are useful for modeling problems involving area and projectile motion. In this section, you will learn how to identify, graph, and solve quadratic functions in various forms. You will also explore some real-world applications of quadratic models, such as finding the maximum height of a rocket or the minimum area of a fence. Regression therapy aims to help you access subconscious memories. We look at the possible benefits and risks. Regression therapy is an intervention that focuses on uncovering subco...Jan 27, 2020 · 1 Answer. Function poly () in R is used to construct orthogonal polynomials. These are equivalent to standard polynomials but are numerically more stable. That is, the two models. m1 <- glmer (FirstSteeringTime ~ poly (startingpos, 2) + (1 | pNum), family = Gamma (link = "identity"), data = data) m2 <- glmer (FirstSteeringTime ~ startingpos + I ... Moderated regression analysis is commonly used to test for multiplicative influences of independent variables in regression models. D. Lubinski and L. G. Humphreys (1990) have shown that significant moderator effects can exist even when stronger quadratic effects are present. They recommend comparing effect sizes associated with both effect types and …Linear functions take the form y=mx+b. Quadratic functions take the form y=ax2+bx+c. Exponential functions take the form y=a⋅bx. In real-world applications, the function that describes some physical situation is not given; it has to be found before the problem can be solved. mation for linear models, but not for quadratic regression (see Carroll et al., 1995). Kuichenhoff & Carroll (1997) have applied SIMEX to estimate the changepoint in segmented linear regression, a problem which is somewhat similar to (although harder than) that of estimating the turning point of a quadratic model.Nov 28, 2016 · As in the section using the poly function above, the same model is being fit, because the family of regression functions is the same — all bivariate quadratic functions of covariates — and the vector subspace of all mean values is the same.But the regression coefficients are different because a different model matrix is used. Hypothesis TestsI'm trying to plot a quadratic regression line in a scatterplot using the following code: bmi is body mass index and pbfm is "percentage body fat content". mod3 <- lm (pbfm ~ bmi + I (bmi^2)) par (mfrow = c (1,1)) plot (bmi, pbfm) lines (bmi, predict (mod3),col="blue",lwd=0.02) It is plotted in the order that the data appears so sort it by …Sep 2, 2020 · To actually perform quadratic regression, we can fit a polynomial regression model with a degree of 2 using the numpy.polyfit () function: import numpy as np #polynomial fit with degree = 2 model = np.poly1d (np.polyfit (hours, happ, 2)) #add fitted polynomial line to scatterplot polyline = np.linspace (1, 60, 50) plt.scatter (hours, happ) plt ... 11 Jan 2024 ... Implementation of Polynomial Regression ... Polynomial Regression is a form of linear regression in which the relationship between the independent ...Nov 17, 2023 · The data points don’t fall along a straight line, suggesting a more complex association between study hours and exam scores. This is where quadratic regression steps in. Fitting the Quadratic Model: Capturing the Curve. To capture the curvature evident in our data, we’ll employ the lm() function in R to fit a quadratic regression model ...Download Table | Threshold estimation-using quadratic regression. from publication: FINANCIAL LIBERALIZATION AND STOCK MARKET EFFICIENCY: MEASURING THE ...Jan 18, 2024 · Degree 1: y = a0 + a1x. As we've already mentioned, this is simple linear regression, where we try to fit a straight line to the data points. Degree 2: y = a0 + a1x + a2x2. Here we've got a quadratic regression, also known as second-order polynomial regression, where we fit parabolas. Degree 3: y = a0 + a1x + a2x2 + a3x3.Quadratic Regression is a process of finding the equation of parabola that best suits the set of data. The equation can be defined in the form as a x 2 + b x + c. Quadratic regression is an extension of simple linear regression. As in the section using the poly function above, the same model is being fit, because the family of regression functions is the same — all bivariate quadratic functions of covariates — and the vector subspace of all mean values is the same.But the regression coefficients are different because a different model matrix is used. Hypothesis TestsLinear regression is the solution to this problem where F is the class of linear functions. 1 Quadratic regression A quadratic function is a function f(x) of the form f(x) = ax2 + bx + c for fixed constants a, b, and c. Note that taking a = 0 we see that a linear function is a special case of a quadratic function. 2 Step 3: Perform quadratic regression. Next, we will perform quadratic regression. Click on the Analyze tab, then Regression, then Linear: In the new window that pops up, drag happiness into the boxed labeled Dependent. Drag hours and hours2 into the box labeled Independent (s). Then click OK.Nov 13, 2021 · 1.2. Linear and Quadratic Discriminant Analysis一、简介西瓜书里是这样介绍的线性判别的：LDA的思想十分朴素：给定训练样例集，设法将样例投影到一条直线上，使得同类样例的投影点尽可能接近、异类样例的投影点尽可能远离；在对新样本进行分类 ...Note that taking a = 0 we see that a linear function is a special case of a quadratic function. 2. Page 2. Large An example of quadratic regression. Find a ...In regression analysis, curve fitting is the process of specifying the model that provides the best fit to the specific curves in your dataset. Curved relationships between variables are not as straightforward to fit and interpret as linear relationships. For linear relationships, as you increase the independent variable by one unit, the mean ...Use logistic regression to fit a model to these data. Use the model to predict the seal population for the year 2020. To the nearest whole number, what is the limiting value of this model? Answer a. The logistic regression model that fits these data is \(y=\dfrac{25.65665979}{1+6.113686306e^{−0.3852149008x}}\). Answer bThis video demonstrates how to conduct a curvilinear regression analysis (quadratic model) in SPSS. Curvilinear regression analysis is useful when there is o...to a quadratic functional regression model, similarly to the situation in ordinary regression. This approach follows the classical strategy to embed an ill-fitting model into a larger class of models. It is thus natural to consider a quadratic regression relationship when moving one step beyond the at University of Toronto Library on April 21 ...Regression Equation. Y i e l d ^ = 7.96 − 0.1537 T e m p + 0.001076 T e m p ∗ T e m p. We see that both temperature and temperature squared are significant predictors for the quadratic model (with p -values of 0.0009 and 0.0006, respectively) and that the fit is much better than the linear fit. From this output, we see the estimated ...Dec 1, 2023 · Moreover, Linear Regression (LR) and Quadratic Regression (QR) Machine Learning (ML) algorithms are proposed to forecast the demand and decrease the possibility of a shortage in the PSCND. We show that QR has better performance than LR in PSCND. In the proposed approach, the demand for medicine is forecasted by the QR technique.Plot quadratic regression with equation displayed. 3. How to plot a horizontal quadratic function? 4. How to plot a linear and quadratic model on the same graph? 4. Quadratic regression line using R plotly. 1. How to create prediction line for Quadratic Model. 0.Explore math with our beautiful, free online graphing calculator. Graph functions, plot points, visualize algebraic equations, add sliders, animate graphs, and more.12 Nov 2015 ... Re: Quadratic Regression Fit ... Hi, Brian! Try using the Fit Special... dialog of the Fit Y by X platform (Under the LIRT by Bivariate Fit).Jan 11, 2022 · Quadratic functions are useful for modeling problems involving area and projectile motion. In this section, you will learn how to identify, graph, and solve quadratic functions in various forms. You will also explore some real-world applications of quadratic models, such as finding the maximum height of a rocket or the minimum area of a fence. Quadratic regression models are often constructed based on certain conditions that must be verified for the model to fit the data well, and to be able to predict accurately. This site also presents useful information about the characteristics of the fitted quadratic function.Nov 17, 2023 · The data points don’t fall along a straight line, suggesting a more complex association between study hours and exam scores. This is where quadratic regression steps in. Fitting the Quadratic Model: Capturing the Curve. To capture the curvature evident in our data, we’ll employ the lm() function in R to fit a quadratic regression model ...Multivariate Quadratic Regression. 0. Regression on trivariate data with one coefficient 0. 0. Interpretation of multiple factor variables in linear regression. Hot Network Questions What legal rights does a german landlord have over you if you stop paying rent?Jan 18, 2024 · Degree 1: y = a0 + a1x. As we've already mentioned, this is simple linear regression, where we try to fit a straight line to the data points. Degree 2: y = a0 + a1x + a2x2. Here we've got a quadratic regression, also known as second-order polynomial regression, where we fit parabolas. Degree 3: y = a0 + a1x + a2x2 + a3x3.Oct 3, 2023 · From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression. Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal, Bhavya Agrawalla. We conduct a comprehensive investigation into the dynamics of gradient descent using large-order constant step-sizes in the context of quadratic regression models.To say a person has “regressive tendencies” is a way of saying that the individual being discussed has a tendency to behave in a less mature, or even childish, manner when he or sh...Step 1: Visualize the data. Before we can use quadratic regression, we need to make sure that the relationship between the explanatory variable (hours) and response variable (happiness) is actually quadratic. First, we will input the data values for both the explanatory and the response variable. Press Stat and then press EDIT .Jan 11, 2022 · Quadratic functions are useful for modeling problems involving area and projectile motion. In this section, you will learn how to identify, graph, and solve quadratic functions in various forms. You will also explore some real-world applications of quadratic models, such as finding the maximum height of a rocket or the minimum area of a fence. This study examines matrix quantile regression where the covariate is a matrix and the response is a scalar and proposes an estimation procedure based on convex regularizations in a high-dimensional setting and an implementation based on the incremental proximal gradient algorithm. This study examines matrix quantile regression where the covariate is …The "regression" part of the name came from its early application by Sir Francis Galton who used the technique doing work in genetics during the 19th century. He was looking at how an offspring's characteristics tended to be between those of the parents (i.e. they regressed to the mean of the parents). The "regression" part just ended up stuck ... Quadratic regression is a method to model a relationship between the two sets of the independent variables. It is referred as the process of determining the ...One way to try to account for such a relationship is through a polynomial regression model. Such a model for a single predictor, X, is: Y = β 0 + β 1 X + β 2 X 2 + … + β h X h + ϵ, where h is called the degree of the polynomial. For lower degrees, the relationship has a specific name (i.e., h = 2 is called quadratic, h = 3 is called ...to a quadratic functional regression model, similarly to the situation in ordinary regression. This approach follows the classical strategy to embed an ill-fitting model into a larger class of models. It is thus natural to consider a quadratic regression relationship when moving one step beyond the at University of Toronto Library on April 21 ...Sep 2, 2020 · Quadratic regression is a type of regression we can use to quantify the relationship between a predictor variable and a response variable when the true relationships is quadratic, which may look like a …Ms. Smith's Math TutorialsYou Try Answer:1) y= - .435x^2 + 24.022x - 48.421 (x^2 is x squared)2) 283.06 yardsChoose the Quadratic Regression Model. Press STAT, arrow right to CALC, and arrow down to 5: QuadReg. Hit ENTER. When the template for QuadReg appears on the home …Feb 7, 2020 · In this paper, we develop statistical inference procedures for functional quadratic quantile regression model in which the response is a scalar and the predictor is a random function defined on a compact set of R. The functional coefficients are estimated by functional principal components. The asymptotic properties of the resulting estimators …Jun 2, 2012 · The secret to doing a quadratic or a cubic regression analysis is defining the Input X Range:. If you're doing a simple linear regression, all you need are 2 columns, X & Y. If you're doing a quadratic, you'll need X_1, X_2, & Y where X_1 is the x variable and X_2 is x^2 ; likewise, if you're doing a cubic, you'll need X_1, X_2, X_3, & Y where ... Learn how to use the quadratic regression formula to fit a line to a set of points on a graph. Explore different forms of the formula, such as standard, vertex, and two-point, and see how they correspond to the graphical representation of the line. See examples of how to use the calculator and the formula with various types of data and functions. Learn how to find the best fit equation for a set of data shaped like a parabola using quadratic regression. The web page explains the simple definition, the equation, the R-squared value, and the steps to calculate or find by hand the equation with a calculator or by hand. It also provides sample problems and tips for using TI-83 or TI-89 calculators. What if I fail my children when it comes to this indefinite time I have with them at home? What if, because of me, they regress? What if I --... Edit Your Post Published by jthreeN...In this paper, a kernel-free quadratic surface support vector regression with non-negative constraints (NQSSVR) is proposed for the regression problem. The task of the NQSSVR is to find a quadratic function as a regression function. By utilizing the quadratic surface kernel-free technique, the model avoids the difficulty of choosing the …Solution. Begin by entering the data into a graphing utility and displaying the scatter plot, as shown in Figure 3.64. From the scatter plot, you can see that the data appears to follow a parabolic pattern. Using the regression feature of a graphing utility, you can find the quadratic model, as shown in Figure 3.65.28 Oct 2010 ... I use ice cream sales data related to high temperature of the day for twenty days and check whether a quadratic regression is reasonable to ...The graph of a quadratic function is a U-shaped curve called a parabola. One important feature of the graph is that it has an extreme point, called the vertex. If the …Move the cursor to y1(x) and then press ENTER. A window will pop up with the a and b for the trigonometric regression equation y = ab x. The trigonometric regression equation will also appear in the y1= line of the Y= screen. This particular regression equation is .93781sin(.46732)*x+2.88273+18.63905. References. Deviant, S. (2009).Jan 18, 2024 · Here we've got a quadratic regression, also known as second-order polynomial regression, where we fit parabolas. Degree 3: y = a 0 + a 1 x + a 2 x 2 + a 3 x 3. This is cubic regression, a.k.a. third-degree polynomial regression, and here we deal with cubic functions, that is, curves of degree 3. ETF strategy - QUADRATIC DEFLATION ETF - Current price data, news, charts and performance Indices Commodities Currencies StocksIn this case, adding a quadratic term to the regression equation may help model the relationship between X and Y. The equation becomes: Y = β0 + β1X + β2X2 Y = β 0 + β 1 X + β 2 X 2. Note that the quadratic model does not require the data to be U-shaped. Other curves can also be fitted using just a part of the parabola, as we see below: We see that both temperature and temperature squared are significant predictors for the quadratic model (with p -values of 0.0009 and 0.0006, respectively) and that the fit is much better than the linear fit. From this …Nov 22, 2017 · The main point is that using quadratic regression to test the presence of a U-shaped relationship is very very wrong. Apparently quadratic fits are often used in some fields to argue in favor of a U-shaped relationship (i.e. t-test for the quadratic term is taken to be the test of U-shape-ness); this is troubling. Here is the key figure:I'm trying to plot a quadratic regression line in a scatterplot using the following code: bmi is body mass index and pbfm is "percentage body fat content". mod3 <- lm (pbfm ~ bmi + I (bmi^2)) par (mfrow = c (1,1)) plot (bmi, pbfm) lines (bmi, predict (mod3),col="blue",lwd=0.02) It is plotted in the order that the data appears so sort it by …Aug 20, 2021 · Once you have your data in a table, enter the regression model you want to try. For a linear model, use y1 y 1 ~ mx1 +b m x 1 + b or for a quadratic model, try y1 y 1 ~ ax2 1+bx1 +c a x 1 2 + b x 1 + c and so on. Please note the ~ is usually to the left of the 1 on a keyboard or in the bottom row of the ABC part of the Desmos keypad. Here you ... Use Quadratic Regression to find a parabola which models the Saturday data and comment on its goodness of fit. Compare and contrast the predictions the three models make for my weight on January 1, 2010 (Day #366). Can any of these models be used to make a prediction of my weight 20 years from now? Explain your answer. 28 Oct 2010 ... I use ice cream sales data related to high temperature of the day for twenty days and check whether a quadratic regression is reasonable to ...Linear regression is the solution to this problem where F is the class of linear functions. 1 Quadratic regression A quadratic function is a function f(x) of the form f(x) = ax2 + bx + c for fixed constants a, b, and c. Note that taking a = 0 we see that a linear function is a special case of a quadratic function. 2
Solution. Begin by entering the data into a graphing utility and displaying the scatter plot, as shown in Figure 3.64. From the scatter plot, you can see that the data appears to follow …. Arceus x download
Aug 20, 2021 · Once you have your data in a table, enter the regression model you want to try. For a linear model, use y1 y 1 ~ mx1 +b m x 1 + b or for a quadratic model, try y1 y 1 ~ ax2 1+bx1 +c a x 1 2 + b x 1 + c and so on. Please note the ~ is usually to the left of the 1 on a keyboard or in the bottom row of the ABC part of the Desmos keypad. Here you ... About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket Press Copyright ...Many of our calculators provide detailed, step-by-step solutions. This will help you better understand the concepts that interest you. eMathHelp: free math calculator - solves algebra, geometry, calculus, statistics, linear algebra, and linear programming problems step by step.Oct 15, 2021 · Step 7: Substitute the value of a, b, c in the Quadratic regression equation. y = ax2 + bx + c. y = -40x2 + (-1x) + 1034.4. y = -40x2 – x + 1034.4. Hence, the Quadratic regression equation of your parabola is y = -40x2 – x + 1034.4. Apart from this, there are various online Quadratic regression calculators that make your task easy and save ... Feb 1, 2020 · A quadratic regression model where the covariate and the response are both functional is considered, which is a reasonable extension of common function-on-function linear regression models. Methods to estimate the coefficient functions, predict unknown response and test significance of the quadratic term are developed in functional principal ...Solution. Begin by entering the data into a graphing utility and displaying the scatter plot, as shown in Figure 3.64. From the scatter plot, you can see that the data appears to follow a parabolic pattern. Using the regression feature of a graphing utility, you can find the quadratic model, as shown in Figure 3.65.Use logistic regression to fit a model to these data. Use the model to predict the seal population for the year 2020. To the nearest whole number, what is the limiting value of this model? Answer a. The logistic regression model that fits these data is \(y=\dfrac{25.65665979}{1+6.113686306e^{−0.3852149008x}}\). Answer bThis video demonstrates how to conduct a curvilinear regression analysis (quadratic model) in SPSS. Curvilinear regression analysis is useful when there is o...Jun 6, 2020 · This function is called the mean-square (mean-quadratic) regression. The simplest case is that of linear mean-square regression, when one looks for the best linear approximation to $ Y $ by means of $ X $, that is, a linear function $ g ( x) = \beta _ {0} + \beta _ {1} x $ for which the expression $ {\mathsf E} ( Y - g ( X) ) ^ {2} $ takes the ...Therefore, the quadratic model is either as accurate as, or more accurate than, the linear model for the same data. Recall that the stronger the correlation (i.e. the greater the accuracy of the model), the higher the R^2. So the R^2 for the quadratic model is greater than or equal to the R^2 for the linear model. Have a blessed, wonderful day! Learn how to find a quadratic regression equation using Desmos.com. One way to try to account for such a relationship is through a polynomial regression model. Such a model for a single predictor, X, is: Y = β 0 + β 1 X + β 2 X 2 + … + β h X h + ϵ, where h is called the degree of the polynomial. For lower degrees, the relationship has a specific name (i.e., h = 2 is called quadratic, h = 3 is called ...Apr 8, 2022 · Interaction between quadratic term and dummy variable. Asked 1 year, 10 months ago. Modified 1 year, 10 months ago. Viewed 348 times. 1. Suppose I have a linear regression: Y = β1 +β2X +β3X2 +β4D Y = β 1 + β 2 X + β 3 X 2 + β 4 D. where D D is a dummy variable that takes value 0 and 1. If I want to examine if the effect of X X on Y Y ...Solution. Begin by entering the data into a graphing utility and displaying the scatter plot, as shown in Figure 3.64. From the scatter plot, you can see that the data appears to follow …9) Press [ENTER] to calculate the quadratic regression. This will also copy the quadratic regression equation to the Y= Editor. To graph the data and the quadratic regression equation: 10) Press [2nd] [Y=] [1] to access the STAT PLOTS menu and edit Plot1. 11) Press [ENTER] to turn On Plot1. .