FINAL_1_27_Scientific Thinking Excel Tutorial Workbook (Student Version) (1).xlsx_safe

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1.1 Getting Started & Excel Interface 1 Download Office 365 2 Install Analysis Toolpack 3 Review Excel Interface 1. Download Office 365 > Goto my.asu.edu > login > My Apps > Search for "Microsoft Office 365" > Click "Download Office 365" button > complete ASU login > Click "± Install Office" button > follow instructions 2. Install Analysis Toolpack On a MAC > Tools > Excel Add-inns > Select Analysis ToolPak checkbox , click OK > Under Data Tab , confirm Data Analysis icon has appeared On a PC > File tab > Options > Add-Ins > Select Excel Add-ins > click Go > Select Analysis ToolPak checkbox , click OK - If prompted that the Analysis ToolPak is not currently installed, click Yes to install it 3. Review Excel Interface - Ribbon Tabs, Tools & Functions - Formula Bar - Worksheet Area: Cells, Columns / Rows, Worksheet Tabs
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Student (Last Name) Student (First Name) Exam 1 (100pts) Everdeen Katniss 84 Montoya Inigo 72 Venkman Peter 71 Ripley Ellen 94
Exam 2 (100pts) Exam 3 (100pts) Total Exam Pts Abs (Total) Abs (>3) Abs Pts (-10pts) EC Proj (5pts) EC Total Total Pts % 82 98 2 2 78 81 2 1 95 67 6 0 92 98 0 0
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1.2 Data Basics Student (Last Name) Student (First Name) Exam 1 (100pts) Exam 2 (100pts) Exam 3 (100pts) 1 Entering Data Venkman Peter 71 95 67 2 Formatting Data Hilton Seamus 76 73 79 3 Basic Calculations Parra Scarlett 92 100 100 Add Humphreys Teo 71 63 70 Subtract Bevan Jena 90 87 94 Multiply Frank Alysha 98 96 100 Divide Burris Kian 89 98 100 If* Tang Woodrow 87 94 91 Sum* Sanderson Owais 45 41 45 4 Sorting Data Mustafa Usama 56 62 62 Everdeen Katniss 84 82 98 Montoya Inigo 72 78 81 Frye Lucie 68 60 67 Bray Neha 85 77 79 Holt Virginia 92 86 72 Joyner Logan 72 78 80 Kinney Ranveer 58 65 76 Harding Sinead 72 68 75 Schroeder Yousif 100 98 98 Walker Elijah 75 82 78 Barajas Brian 76 85 92 Conner Aj 72 72 81 Granger Hermione 98 100 100 Hanson Rishi 73 69 80 Mcmahon Kimberly 66 74 79 Mata Kris 83 82 87 Hodson Farah 76 72 83 Byers Rikki 65 63 69 Ripley Ellen 94 92 98 Winters Scarlet 87 93 95 Castro Jodi 77 69 75 Kent Maiya 71 70 68
Total Exam Pts Abs (Total) Abs (>3) Abs Pts (-10pts) EC Proj (5pts) EC Total Total Pts % 6 0 5 2 5 1 4 3 4 2 4 1 3 0 3 0 3 0 2 3 2 2 2 1 2 1 2 0 1 3 1 3 1 3 1 2 1 1 1 1 1 0 1 0 0 3 0 3 0 3 0 1 0 1 0 1 0 0 0 0 0 0 0 0
1.3 Functions Student (Last Name) Student (First Name) Exam 1 (100pts) Exam 2 (100pts) 1 Mean (AVERAGE) Barajas Brian 76 85 2 MODE Bevan Jena 90 87 3 MEDIAN Bray Neha 85 77 4 MAX Burris Kian 89 98 5 MIN Byers Rikki 65 63 6 COUNT (#'s only) # who did EC? Castro Jodi 77 69 7 COUNTA (anything) # of students? Conner Aj 72 72 8 COUNTIF (criteria) Freshman Everdeen Katniss 84 82 Sophomore Frank Alysha 98 96 Junior Frye Lucie 68 60 Senior Granger Hermione 98 100 Students w/ >3 Abs? Hanson Rishi 73 69 Students w/ Total% <70%? Harding Sinead 72 68 Hilton Seamus 76 73 Hodson Farah 76 72 Holt Virginia 92 86 Humphreys Teo 71 63 Joyner Logan 72 78 Kent Maiya 71 70 Kinney Ranveer 58 65 Mata Kris 83 82 Mcmahon Kimberly 66 74 Montoya Inigo 72 78 Mustafa Usama 56 62 Parra Scarlett 92 100 Ripley Ellen 94 92 Sanderson Owais 45 56 Schroeder Yousif 100 98 Tang Woodrow 87 94 Venkman Peter 71 95 Walker Elijah 75 82 Winters Scarlet 87 93 Mean: Mode: Median: Max: Min:
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Exam 3 (100pts) Abs Pts (-10 over 3) EC Total Total Pts % Class 92 0 253 84% Sophomore 94 -10 10 271 90% Senior 79 0 241 80% Freshman 100 0 287 96% Junior 69 0 5 202 67% Freshman 75 0 221 74% Freshman 81 0 225 75% Sophomore 98 -10 10 264 88% Freshman 100 0 5 299 100% Sophomore 67 0 5 200 67% Sophomore 100 0 15 313 104% Freshman 80 -20 15 217 72% Senior 75 0 10 225 75% Junior 79 0 10 238 79% Freshman 83 -10 5 226 75% Junior 72 0 15 265 88% Sophomore 70 0 15 219 73% Freshman 80 0 15 245 82% Junior 68 0 209 70% Freshman 76 0 15 214 71% Junior 87 0 5 257 86% Sophomore 79 -20 15 214 71% Junior 81 0 5 236 79% Freshman 62 0 15 195 65% Sophomore 100 0 5 297 99% Freshman 98 0 284 95% Junior 65 0 166 55% Sophomore 98 0 5 301 100% Freshman 91 0 272 91% Sophomore 67 0 233 78% Sophomore 78 -30 5 210 70% Senior 95 0 275 92% Sophomore Mean: Mode: Median: Max: Min:
1.4 Frequency Function, Table, & Plot 1 Calculate Absolute Frequency Sample # 2 Calculate Relative Frequency Sample # / Total 3 Create Frequency Table using Frequency function 4 Plot Frequency Distribution (Histogram) 1. Calculate Absolute Frequency - Class Year Year Freq - Use COUNTIF to calculate frequency of each class year (Column D) Freshman Sophomore Junior Senior 2. Calculate Relative Frequency - Class Year Year Freq - Use COUNTA function to calculate total students (cell D24) Freshman - Calculate relative frequency by dividing frequency of each year by total students Sophomore Junior Senior TOTAL Students 3. Create Frequency Table - Freq of Exam 1 Grades Grade Bins - Create grade "Bins" (values <=, or "up to value" receive that grade) (0-59) E - Use FREQUENCY function to count frequency of each grade (60-69) D (70-79) C (80-89) B (90-100) A 4. Plot Freq Dist (histogram) - Freq of Exam Grades - Highlight frequency of grades values from #3 (Column E) - Goto Insert tab > Column chart - To correct labels, Right-click X-Axis > Goto Selet Data… > Click icon - Change Horizontal (Category) axis labels > Highlight Grade labels (Column C), Hit Return
Student (Last Name) Student (First Name) Exam 1 (100pts) Class Barajas Brian 76 Sophomore Bevan Jena 90 Sophomore Bray Neha 85 Sophomore Burris Kian 89 Junior Byers Rikki 65 Senior Castro Jodi 77 Senior Conner Aj 72 Freshman Everdeen Katniss 84 Junior Frank Alysha 98 Junior Frye Lucie 68 Sophomore Granger Hermione 98 Junior Hanson Rishi 73 Sophomore Harding Sinead 72 Freshman Hilton Seamus 76 Freshman Hodson Farah 76 Sophomore Holt Virginia 92 Junior Humphreys Teo 71 Freshman Joyner Logan 72 Senior Kent Maiya 71 Sophomore Kinney Ranveer 58 Freshman Freq Mata Kris 83 Freshman Mcmahon Kimberly 66 Junior Montoya Inigo 72 Freshman Mustafa Usama 56 Sophomore Parra Scarlett 92 Sophomore Ripley Ellen 94 Sophomore Sanderson Owais 45 Freshman Schroeder Yousif 100 Freshman Tang Woodrow 87 Junior Venkman Peter 71 Sophomore Walker Elijah 75 Freshman Winters Scarlet 87 Freshman
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1.5 Saving Charts 1 Saving Chart as image 1. Saving Chart as image Exam 1 Class Gra > Click on Chart Grade Bins > Right Click again on chart > "Save as Picture…" (0-59) E 59 > Navigate to folder to save in (60-69) D 69 > Goto "Save as Type" dropdown menu > Select PNG (70-79) C 79 (80-89) B 89 (90-100) A 100
Grades Freq 3 3 13 6 7
1.6 Calculate NPD Parameters 1 Mean (AVERAGE) 2 Standard Deviation (STDEV) 1 & 2 Calculate Mean and StdDev Mean StdDev - Calculate Mean & Standard Deviation for each data set. Exam 1 Exam 2 Exam 3 82.5 12.1
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Student Name (Last, First) Exam 1 (100pts) Exam 2 (100pts) Exam 3 (100pts) Barajas, Brian 76 85 92 Bevan, Jena 90 87 94 Bray, Neha 85 77 79 Burris, Kian 89 98 100 Byers, Rikki 65 63 69 Castro, Jodi 77 69 75 Conner, Aj 72 72 81 Everdeen, Katniss 84 82 98 Frank, Alysha 98 96 100 Frye, Lucie 68 60 67 Granger, Hermione 98 100 100 Hanson, Rishi 73 69 80 Harding, Sinead 72 68 75 Hilton, Seamus 76 73 79 Hodson, Farah 76 72 83 Holt, Virginia 92 86 72 Humphreys, Teo 71 63 70 Joyner, Logan 72 78 80 Kent, Maiya 71 70 68 Kinney, Ranveer 58 65 76 Mata, Kris 83 82 87 Mcmahon, Kimberly 66 74 79 Montoya, Inigo 72 78 81 Mustafa, Usama 56 62 62 Parra, Scarlett 92 100 100 Ripley, Ellen 94 92 98 Sanderson, Owais 45 56 65 Schroeder, Yousif 100 98 98 Tang, Woodrow 87 94 91 Venkman, Peter 71 95 67 Walker, Elijah 75 82 78 Winters, Scarlet 87 93 95
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1.7 Estimate Probability using a Normal Probability Distribution 1 Determine Mean & Standard Deviation of Sample Population * 2 What is the probability that a student is 68" or less? 3 What is the probability that a student is 68" or greater? *Assume sample poulation is normally distributed 1 Mean and StdDev of Sample Pop - Determine Mean & StdDev Mean: StdDev: 2 Prob (height <= x")? - Determine probability using NORM.DIST function, cumulative Height: 68.0 P(x<=68): 3 Prob (height >= x")? - Determine probability using 1-(NORM.DIST) function, cumulative Height: 68.0 P(x>=68):
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Student Height (in) Barajas, Brian 69.9 Bevan, Jena 67.8 Bray, Neha 64.8 Burris, Kian 60.9 Byers, Rikki 64.6 Castro, Jodi 69.9 Conner, Aj 75.7 Everdeen, Katniss 62.6 Frank, Alysha 65.2 Frye, Lucie 66.4 Granger, Hermione 68.7 Hanson, Rishi 68 Harding, Sinead 67.2 Hilton, Seamus 64.5 Hodson, Farah 66.9 Holt, Virginia 59.2 Humphreys, Teo 69.3 Joyner, Logan 64.5 Kent, Maiya 59.8 Kinney, Ranveer 62.8 Mata, Kris 70.2 Mcmahon, Kimberly 64.3 Montoya, Inigo 62.5 Mustafa, Usama 70.7 Parra, Scarlett 70.1 Ripley, Ellen 68 Sanderson, Owais 62.3 Schroeder, Yousif 66.7 Tang, Woodrow 62.4 Venkman, Peter 59.4 Walker, Elijah 65 Winters, Scarlet 65.6
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1.8 Scatter Plots and Linear Models Student Avg Study Time (Hrs/wk) Exam 1 (100pts) 1 Create a Scatter Plot Barajas, Brian 3.0 76 - To Create SCATTER PLOT Bevan, Jena 1.0 65 > Highlight Study Time & Exam Scores Bray, Neha 5.0 98 (Including headers - F2:G34) Burris, Kian 3.0 55 > Goto Insert tab > X Y (Scatter) Byers, Rikki 3.0 65 > Choose first Scatter option Castro, Jodi 3.0 76 - To CHANGE Scale on X-Axis Conner, Aj 4.5 75 > Click X-Axis, Right-Click > Format Axis... Everdeen, Katniss 4.5 85 > Edit Minimum & Maximum Bounds Frank, Alysha 4.0 90 - To ADD Additional Elements to Plot Frye, Lucie 4.0 77 > Click on Chart > Goto Chart Design tab Granger, Hermione 6.0 89 > Add Chart Element Hanson, Rishi 2.5 66 - Axes, Titles, Labels, Legend, etc. Harding, Sinead 3.0 89 Hilton, Seamus 3.0 83 2 Add Linear Regression Line & Equation Hodson, Farah 0.5 72 - To ADD Linear Regression Line & Equation: Holt, Virginia 2.0 68 > Click on Chart > Goto Chart Design tab Humphreys, Teo 5.0 71 > Add Chart Element > Trendline > Linear Joyner, Logan 5.0 85 > Click to highlight Trendline, then Right-Click Kent, Maiya 4.0 85 > Format Trendline … Kinney, Ranveer 1.0 45 > Click checkboxes: Mata, Kris 5.0 85 - Display Equation on chart Mcmahon, Kimberly 1.0 58 - Display R-squared value on chart Montoya, Inigo 4.0 65 Mustafa, Usama 4.5 73 Parra, Scarlett 2.0 87 3 Calculate the Parameters of a Linear Model Ripley, Ellen 6.0 92 SLOPE =SLOPE (known_ys, known_xs) Sanderson, Owais 3.5 76 INTERCEPT =INTERCEPT (known_ys, known_xs) Schroeder, Yousif 2.0 72 Std Err Reg (STDDEV) =STEYX (known_ys, known_xs) Tang, Woodrow 4.0 71 Venkman, Peter 0.5 56 Walker, Elijah 4.0 92 Winters, Scarlet 6.5 87
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1.9 Using linear models to make predictions Student Avg Study Time (Hrs/wk) SLOPE =SLOPE (known_ys, known_xs) 0.00 Barajas, Brian 3.0 INTERCEPT =INTERCEPT (known_ys, known_xs) 0.00 Bevan, Jena 1.0 Std Err Reg (STDDEV) =STEYX (known_ys, known_xs) 0.00 Bray, Neha 5.0 Burris, Kian 3.0 Byers, Rikki 3.0 1 Calculate Predicted Outcomes based on LM Castro, Jodi 3.0 Mean Exam 1 score? Conner, Aj 4.5 Mean Avg Study Time? Everdeen, Katniss 4.5 1.1 Estimate based on scatter plot linear regression line: Frank, Alysha 4.0 Predicted Exam 1 score for student avg 3 hrs/wk study time? Frye, Lucie 4.0 Predicted Exam 1 score for student avg 1 hrs/wk study time? Granger, Hermione 6.0 Predicted Exam 1 score for student avg 6 hrs/wk study time? Hanson, Rishi 2.5 Harding, Sinead 3.0 1.2 Calculate using linear function: =(slope * x) + intercept Hilton, Seamus 3.0 Predicted Exam 1 score for student avg 3 hrs/wk study time? Hodson, Farah 0.5 Predicted Exam 1 score for student avg 1 hrs/wk study time? Holt, Virginia 2.0 Predicted Exam 1 score for student avg 6 hrs/wk study time? Humphreys, Teo 5.0 (Avg Study Time hrs/wk) x = Joyner, Logan 5.0 (Exam 1 score) y = Kent, Maiya 4.0 Kinney, Ranveer 1.0 Mata, Kris 5.0 2 Calculate Probability of Value based on LM Mcmahon, Kimberly 1.0 Montoya, Inigo 4.0 Mustafa, Usama 4.5 Parra, Scarlett 2.0 Ripley, Ellen 6.0 Sanderson, Owais 3.5 Schroeder, Yousif 2.0 Tang, Woodrow 4.0 Venkman, Peter 0.5 Walker, Elijah 4.0 =1 - NORM.DIST(score, ŷ given x , SER ,1) Winters, Scarlet 6.5 =1 - NORM.DIST(score, mean, stdev,1) Probability of scoring >= 80 for student avg 3 hrs/wk ? Probability of scoring >= 90 for student avg 3 hrs/wk? Probability of scoring >= 90 for student avg 6 hrs/wk ?
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Exam 1 (100pts) 76 65 98 55 65 76 75 85 90 77 89 66 89 83 72 68 71 85 85 45 85 58 65 73 87 92 76 72 71 56 92 87
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1.10 Predicting Variable with a Linear Function Set up a worksheet to quickly predict a variable given the slope and intercept of a linear function. 1 Solve for x given y 2 Solve for y given x 3 Solve for x given change in y 4 Solve for y given change in x Function LF Parameters y = ax + b slope (a) 0.6 y = dependent variable intercept (b) -48 x = independent variable a = slope b = intercept 1. Solve for x given y y = ax + b y = 45 --> y - b = ax x = 155 --> (y - b) / a = x 2. Solve for y given x y = ax + b x = 23 y = -34 3. Solve for x given a change in y y 1 - y 2 = a(x 1 - x 2 ) change in y = 20 --> ᵵ y = a ( ᵵ x ) change in x = 33 --> ᵵ y / a = ᵵ x 4. Solve for y given a change in x y 1 - y 2 = a(x 1 - x 2 ) change in x = 33 --> ᵵ y = a ( ᵵ x ) change in y = 20
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1.11 Linear Model - Categorical Variable 1 Dummy Code - Dummy code for Column F (Chem Pre-req) 0 = NO chem, 1 = chem (hint: us if function to automate coding. Example: =if(F2="chem",1,0) 2 Calculate Mean 0,1: PLOT -> LINEAR MODEL & EQUATION Mean 0 (No Chem): Mean of Y when X = 0 (equals the intercept) Mean 1 (Chem): Mean of Y when X = 1 (equals the intercept + slope) 3 Calculate Mean 0,1: DIRECT -> LINEAR FUNCTION SLOPE =SLOPE (known_ys, known_xs) INTERCEPT =INTERCEPT (known_ys, known_xs) Std Err Reg (STDDEV) =STEYX (known_ys, known_xs) Mean 0 (No Chem): Mean of Y when X = 0 (equals the intercept) No Chem Mean 1 (Chem): Mean of Y when X = 1 (equals the intercept + slope) Chem 4 PLOT BAR GRAPH w/Error Bars
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Student Name (Last, First) Chem Pre-req? Dummy Code +/- chem Total Exam % Barajas, Brian chem 84% Bevan, Jena 90% Bray, Neha 80% Burris, Kian chem 96% Byers, Rikki 66% Castro, Jodi 74% Conner, Aj chem 75% Everdeen, Katniss chem 88% Frank, Alysha chem 98% Frye, Lucie 65% Granger, Hermione chem 99% Hanson, Rishi 74% Harding, Sinead 72% Hilton, Seamus chem 76% Hodson, Farah 77% Holt, Virginia 83% Humphreys, Teo chem 68% Joyner, Logan 77% Kent, Maiya 70% Kinney, Ranveer 66% Mata, Kris chem 84% Mcmahon, Kimberly 73% Montoya, Inigo 77% Mustafa, Usama 60% Parra, Scarlett chem 97% Ripley, Ellen chem 95% Sanderson, Owais 55% Schroeder, Yousif 99% Tang, Woodrow 91% Venkman, Peter chem 78% Walker, Elijah chem 78% Winters, Scarlet 92%
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Calculation COUNT COUNTA COUNTIF FREQUENCY IF Intercept MAX mean MEDIAN MIN MODE NORM.DIST Slope STDEV STEYX SUM
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Description Returns the number of numerical values in a supplied set of cells or values Returns the number of non-blanks in a supplied set of cells or values Returns the number of cells (of a supplied range), that satisfy a given criteria Returns an array showing the number of values from a supplied array, which fall into specified ranges Tests a user-defined condition and returns one result if the condition is TRUE, and another result if the condition is FALSE Returns the intercept of a linear model of the relationship between two continuous variables (also called the intercept of a linear function or a regression line) Returns the largest value from a list of supplied numbers Estimate mean of a variable from a sample Returns the Median (the middle value) of a list of supplied numbers Returns the smallest value from a list of supplied numbers Returns the Mode (the most frequently occurring value) of a list of supplied numbers Returns the normal cumulative distribution Returns the slope of a linear model of the relationship between two continuous variables (also called the slope of a linear function or a regression line) Returns the standard deviation of a supplied set of values (which represent a sample of a population) Returns the standard deviation of a linear model of the relationship between two continuous variables (also called the standard error of a regression line) Returns the sum of a supplied list of numbers
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Syntax =COUNT( value1, [value2], ... ) =COUNTA( value1, [value2], ... ) =COUNTIF( range, criteria ) =FREQUENCY( data_array, bins_array ) =IF( logical_test, value_if_true, value_if_false ) =INTERCEPT (known_ys, known_xs) =MAX( number1, [number2], ... ) =AVERAGE( number1, [number2], ... ) =MEDIAN( number1, [number2], ... ) =MIN( number1, [number2], ... ) =MODE( number1, [number2], ... ) =NORM.DIST( x, mean, standard_dev, cumulative ) =SLOPE (known_ys, known_xs) =STDEV( number1, [number2], ... ) =STEYX (known_ys, known_xs) =SUM( number1, [number2], ... )
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