# Download REGRESSION AND ANALYSIS OF VARIANCE 1 - STA331 Past Question PDF

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REGRESSION AND ANALYSIS OF VARIANCE 1 past question for the year 2019 examines 300-level Science and Technology students of FUTO, offering STA331 course on their knowledge of regression, variance, linear model, partial correlation

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## Past Questions related to REGRESSION AND ANALYSIS OF VARIANCE 1

Year: 2018

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: CSC510

Topics: model development, mathematical model, markov model, exponential model, cubic model, inverse model, CPU, linear programming, allocation problem

Year: 2019

School: University of Ilorin

Department: Administration, Social and Management science

Course Code: ACC233, FIN233

Topics: correlation, least square regression, time series model, regression model, statistical analysis, forecasting, hypothesis, price index, quantity index, sampling error, population, sampling, product moment correlation coefficient, finite population, estimators, point estimates, error

Year: 2018

School: University of Ilorin

Department: Science and Technology

Course Code: STA204

Topics: product moment correlation coefficient, t-table, standard deviation, confidence interval, significance level, Pearson correlation coefficient, estimator, estimate, hypothesis

Year: 2019

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: EVT407

Topics: statistical interference, experimental error, pig, piggery, fish, linear model, ANOVA, Bernoulli trial

Year: 2018

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: MTH101

Topics: logarithm, partial fraction, inequality, linear expansion, complex number, arithmetic progression, geometric progression

Year: 2018

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: STA211

Topics: probability, frequency, data plane, scattergram, area plot, histogram, class boundaries, regression, skewness

Year: 2019

School: University of Ilorin

Department: Science and Technology

Course Code: MAT308

Topics: modelling, model, Mathematical model, Malthus model

Year: 2015

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: MTH101

Topics: Range, partial fractions

Year: 2018

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: CSC304

Topics: random file, direct file, data file, file attributes, file, exhaustive index, partial index, index

Year: 2019

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: EVT519

Topics: climate change, global warming, greenhouse effect, emission accounting, emission, global temperature model

Year: 2019

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: PHY413

Topics: sommerfield theory, electron gas, energy level, superconductivity, meissner effect, isotopic mass, bloch function, kronig-penny model, insulators, diamagnetism, ferromagnetism, antiferromagnetism

Year: 2020

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: MTH101

Topics: Logarithm, indices, combination, complex numbers, binomial expansion, geometric progression, inequalities, partial fraction, remainder theorem

Year: 2020

School: University of Benin

Department: Science and Technology

Course Code: MTH110

Topics: Sets, binary operation, partial fractions, mathematical induction, real numbers, remainder theorem, factor theorem, polynomial, mapping, complex number, Argand diagram, trigonometric function, sequence, series, recurrency, D'Alembert ratio test, permutation, combination

Year: 2019

School: Federal University of Technology, Minna

Department: Science and Technology

Course Code: MAT111

Topics: partial fraction, set theory, mapping, function, Binomial theorem

### Books related to REGRESSION AND ANALYSIS OF VARIANCE 1

Author: Elizabeth Peck, Geoffrey Vining, Douglas Montgomery

Department: Science and Technology

Course Code: STA351

Topics: Linear Regression Analysis, Regression, Model Building, Data Collection, Simple Linear Regression Model, Simple Linear Regression, Least-Squares Estimation, Hypothesis Testing, Interval Estimation, Multiple Regression Models, Multiple linear regression, Hypothesis Testing, Confidence Intervals, Standardized Regression Coefficients, Multicollinearity, Residual Analysis, model adequacy checking, Variance-Stabilizing Transformations, Generalized Least Squares, Weighted Least Squares, Regression Models, subsampling, Leverage, Measures of Influence, influence, Polynomial regression Models, Piecewise Polynomial Fitting, Nonparametric Regression, Kernel Regression, Locally Weighted Regression, Orthogonal Polynomials, Indicator Variables, Multicollinearity, Multicollinearity Diagnostics, Model-Building, regression models, Linear Regression Models, Nonlinear Regression Models, Nonlinear Least Squares, Logistic Regression Models, Poisson regression, Time Series Data, Detecting Autocorrelation, Durbin-Watson Test, Time Series Regression, Robust Regression, Inverse Estimation

Author: Ann Ryan, Douglas Montgomery, Elizabeth Peck, Geoffrey Vining

Department: Science and Technology

Course Code: STA351

Topics: Linear Regression Analysis, Regression, Model Building, Data Collection, Simple Linear Regression Model, Simple Linear Regression, Least-Squares Estimation, Hypothesis Testing, Interval Estimation, Multiple Regression Models, Multiple linear regression, Hypothesis Testing, Confidence Intervals, Standardized Regression Coefficients, Multicollinearity, Residual Analysis, model adequacy checking, Variance-Stabilizing Transformations, Generalized Least Squares, Weighted Least Squares, Regression Models, subsampling, Leverage, Measures of Influence, influence, Polynomial regression Models, Piecewise Polynomial Fitting, Nonparametric Regression, Kernel Regression, Locally Weighted Regression, Orthogonal Polynomials, Indicator Variables, Multicollinearity, Multicollinearity Diagnostics, Model-Building, regression models, Linear Regression Models, Nonlinear Regression Models, Nonlinear Least Squares, Logistic Regression Models, Poisson regression, Time Series Data, Detecting Autocorrelation, Durbin-Watson Test, Time Series Regression, Robust Regression, Inverse Estimation

Author: Alaba Oluwayemisi Oyeronke

Department: Science and Technology

Course Code: STA322

Topics: Regression, Analysis of Variance, Correlation Coefficient, Correlation Ratio, Simple Linear Regression, Multiple Linear Regression, Multiple Regression Analysis, Polynomial Regression, Non-Linear Regression Model, ANOVA, Randomized Complete Block Design, Analysis of Variance for Randomized Complete Block Design, Latin Square Design, Least Significant Difference

Author: John Freund, Benjamin Perles

Department: Science and Technology

Course Code: STA121

Topics: statistics, summarizing data, listing, grouping, Numerical data, Stem-and-Leaf Displays, frequency distribution, graphical presentations, Summarizing Two-Variable Data, population, sample, mean, weighted mean, median, mode, grouped data, measures of variation, range, standard deviation, variance, posibility, probability, counting, permutation, combination, sample spaces, events, odds, conditional probability, multiplication rules, Bayes theorem, Mathematical Expectation, expectation, decision, decision making, Statistical Decision Problems, Random Variable, probability distributions, binomial distributions, hypergeometric distributions, Poisson distribution, Multinomial distribution, Mean of a Probability Distribution, Standard Deviation of a Probability Distribution, normal distribution, Continuous Distributions, sampling, random sampling, sample designs, systematic sampling, stratified sampling, cluster sampling, sampling distribuions, central limit theorem, estimation, Tests of Hypotheses, Significance Tests, One-Way Analysis of Variance, analysis of variance, Multiple Comparisons, Two-Way Analysis of Variance, Design of Experiments, Design of Experiments, regression, Curve Fitting, Regression Analysis, Multiple Regression, Nonlinear Regression, Coefficient of Correlation, correlation, Correlation Analysis, Multiple Correlation, Partial Correlation, Nonparametric tests, sign test, signed-rank test, U test, H test, Tests of Randomness, Rank Correlation

Author: Michael Kutner, Christopher Nachtsheim, John Neter, William Li

Department: Science and Technology

Course Code: STA322

Topics: Linear Statistical Models, linear regression, inference, correlation analysis, simultaneous inferences, regression analysis, simple linear regression analysis, multiple regression, quantitative predictors, qualitative predictors, regression, model, autocorrelation, time series, nonlinear regression, Neural networks, Logistic regression, Possion regression, Generalized linear models, ANOVA, Two-factor analysis of variance, two-factor studies, randomized complete block designs, analysis of covariance, multifactor studies, Nested designs, subsampling, partially nested designs

Author: Michael Kutner, Christopher Nachtsheim, John Neter, William Li

Department: Science and Technology

Course Code: STA322

Topics: Linear Statistical Models, linear regression, inference, correlation analysis, simultaneous inferences, regression analysis, simple linear regression analysis, multiple regression, quantitative predictors, qualitative predictors, regression, model, autocorrelation, time series, nonlinear regression, Neural networks, Logistic regression, Possion regression, Generalized linear models, ANOVA, Two-factor analysis of variance, two-factor studies, randomized complete block designs, analysis of covariance, multifactor studies, Nested designs, subsampling, partially nested designs

Department: Science and Technology

Course Code: STA351

Topics: generalized linear models, Model Fitting, Exponential Family, estimation, inference, normal linear models, Binary Variables, Logistic Regression, Nominal Logistic Regression, Ordinal Logistic Regression, Poisson Regression, Log-Linear Models, Survival Analysis, Clustered data, Longitudinal Data, Bayesian Analysis, Markov Chain Monte Carlo Methods

Author: MK Garba

School: University of Ilorin

Department: Science and Technology

Course Code: STA204

Topics: Correlation, simple regression, Correlation Analysis, Scatter Plot, Correlation Coefficient

Author: Damodar Gujarati, Dawn Porter

School: Modibbo Adama University of Technology

Department: Administration, Social and Management science

Course Code: CC312

Topics: Single-Equation Regression Models, Regression Analysis.Two-Variable Regression Analysis, Two-Variable Regression Model, Classical Normal Linear Regression Model, Two-Variable Regression, Interval Estimation, Hypothesis Testing, Multiple Regression Analysis, Dummy Variable Regression Models, Multicollinearity, Heteroscedasticity, Autocorrelation, Econometric Modeling, Nonlinear Regression Model, Qualitative Response Regression Models, Panel Data Regression Models, Dynamic Econometric Models, Autoregressive Lag Models, Distributed-Lag Models, Simultaneous-Equation Models, Time Series Econometrics, Simultaneous-Equation Models, Identification Problem, Simultaneous-Equation Methods, Time Series Econometrics, Time Series Econometrics, Forecasting, econometrics

Author: Damodar Gujarati, Dawn Porter

School: Modibbo Adama University of Technology

Department: Administration, Social and Management science

Course Code: CC312

Topics: econometrics, Single-Equation Regression Models, Regression Analysis.Two-Variable Regression Analysis, Two-Variable Regression Model, Classical Normal Linear Regression Model, Two-Variable Regression, Interval Estimation, Hypothesis Testing, Multiple Regression Analysis, Dummy Variable Regression Models, Multicollinearity, Heteroscedasticity, Autocorrelation, Econometric Modeling, Nonlinear Regression Model, Qualitative Response Regression Models, Panel Data Regression Models, Dynamic Econometric Models, Autoregressive Lag Models, Distributed-Lag Models, Simultaneous-Equation Models, Time Series Econometrics, Simultaneous-Equation Models, Identification Problem, Simultaneous-Equation Methods, Time Series Econometrics, Time Series Econometrics, Forecasting

Author: Samuel Olumuyiwa Olusanya

School: National Open University of Nigeria

Department: Administration, Social and Management science

Course Code: ECO355

Topics: Econometrics, Econometrics Model, Linear Regression, Regression Analysis, Ordinary Least Square Method Estimation, Classical Least Regression Method, Ordinary Least Square Estimators, Coefficient of Determination, Classical Normal Linear Regression Model, NORMAL LINEAR REGRESSION MODEL, SINGLE- EQUATION REGRESSION MODELS, ECONOMETRICS ANALYSIS, Method Of Maximum Likelihood, Confidence intervals, Regression Coefficients, Regression Analysis, Analysis of Variance, Normality

Author: Michael Evans, Jeffrey Rosenthal

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: STA301

Topics: probability models, Conditional Probability, Venn diagram, Random Variables, Discrete Distributions, Continuous Distributions, Cumulative Distribution Functions, Joint Distributions, Simulating Probability Distributions, expectation, Inequalities, Jensen’s Inequality, Sampling Distributions, Limits, Central Limit Theorem, Monte Carlo Approximations, Normal Distribution Theory, Chi-Squared Distribution, Statistical Inference, statistical model, Data Collection, Finite Populations, Simple Random Sampling, Histograms, Survey Sampling, Descriptive Statistics, Plotting Data, Likelihood Inference, Maximum Likelihood Estimation, Distribution-Free Methods, Bayesian Inference, Bayesian Computations, Optimal Inferences, Optimal Unbiased Estimation, Optimal Hypothesis Testing, quantitative response, Simple Linear Regression Model, Bayesian Simple Linear Model, Multiple Linear Regression Model, Markov Chains, Gambler’s Ruin Problem, Markov Chain Monte Carlo, Martingales, Brownian Motion, Poisson Processes

Author: Konstantin Zuev

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: STA221

Topics: Statistical Inference, summarizing data, simple random sampling, population variance, normal approximation, confidence intervals, inference, Maximum Likelihood, Hypothesis Testing, Wald test, t-test, Permutation Test, Likelihood Ratio Test, Testing Mendel’s Theory, Multiple Testing, Regression Function, Regression Model, Scatter Plots, Simple Linear Regression Model, Ordinary Least Squares, Interval Estimation, Prediction, Graphic Residual Analysis

Author: Ronald Weiers

Department: Science and Technology

Course Code: STA351

Topics: Business Statistics, data collection, sampling methods, probability, discrete probability distribution, continous probability distributions, sampling distributions, estimation, hypothesis testing, hypothesis tests, analysis of variance, Chi-square applications, nonparametric methods, regression, simple linear regression, correlation, multiple regression, multiple correlation, model building, time series, forecasting, decision theory, total quality management

Author: Steven Thompson

Department: Science and Technology

Course Code: STA351

Topics: Sampling, Sampling Units, Sampling errors, Nonsampling Errors, Simple Random Sampling, Confidence Intervals, Sample Size, Estimating Proportions, Estimating Ratios, Estimating Subpopulation Means, Unequal Probability Sampling, Horvitz-Thompson Estimator, Hansen–Hurwitz Estimator, Auxiliary Data, Ratio Estimation, Ratio Estimator, Small Population Illustrating Bias, Regression Estimation, Linear Regression Estimator, regression model, Multiple Regression Models, Regression Models, Stratified Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Sampling, Multistage Designs, Double Sampling, Two-Phase Sampling, Network Sampling, Link-Tracing Designs, Detectability, Capture–Recapture Sampling, Line-Intercept Sampling, spatial sampling, Spatial Prediction, Kriging, Spatial Covariance Function, Spatial Designs, Adaptive Sampling Designs, Adaptive Sampling, Adaptive Cluster Sampling, Stratified Adaptive Cluster Sampling

Author: STA FUTO

School: Federal University of Technology, Owerri

Department: Science and Technology

Course Code: STA431

Topics: Statistical Computing, file management, data entry, Data Manipulation, sort data, Standardize data, Descriptive Statistics, Chi-square, Frequencies, Normality test, graphs icon, scatter plot, error bars, histogram, Bar Chart, Line Graph, Pie Chart, Paired-sample t-test, ANOVA, Pearson's Correlation, Spearman's Correlation, covariance, linear regression, Straight Line Fit, Curved Line Fit, Multiple Linear Regression

Author: Lyman Ott, Michael Longnecker

School: University of Ilorin

Department: Science and Technology

Course Code: STA124, STA131, STA204, STA224, STA351, ECN414, STA433

Topics: Experimental Studies, Data Description, Probability, Probability Distributions, Population Central Values, inference, Multiple Comparisons, Categorical Data, Linear Regression, Correlation, Multiple Regression, General Linear Model, Analysis of Variance, Analysis of Covariance, Split-Plot, Repeated Measures, Crossover Designs

Author: Miroslav Kaps, William Lamberson

School: National Open University of Nigeria

Department: Agriculture and Veterinary Medicine

Course Code: AGR302

Topics: Biostatistics, measures of central tendency, measures of variability, probability, permutations, random variables, Bernoulli Distribution, Binomial Distribution, Geometric Distribution, Hyper-geometric Distribution, Negative Binomial Distribution, Poisson Distribution, Multinomial Distribution, Uniform Distribution, Normal Distribution, Multivariate Normal Distribution, Logistic Distribution, Chi-square Distribution, Gamma Distribution, Exponential Distribution, Beta Distribution, Dirichlet Distribution, population sample, central limit theorem, degrees of freedom, parameters estimation, point estimation, maximum likelihood estimation, interval estimation, hypothesis testing, simple linear regression, simple regression model, partitioning total variability, partial correlation, rank correlation, likelihood ratio test, robust regression, curvilinear regression, polynomial regression, segmented regression, Least Significance Difference, intraclass correlation, experimental error, randomized complete block design, Partitioning Total Variability

Author: CR Kothari

School: Federal University of Technology, Owerri

Department: Engineering

Course Code: ABE401

Topics: Research methodology, research, research methods, research process, research problem, research design, sampling design, census, sample survey, measurement, scaling techniques, scaling, scaling construction techniques, data collection, observation method, case study method, measures of central tendency, measures of dispersion, measures of asymmetry, skewness, simple regression, regression analysis, multiple correlation, regression, partial correlation, sampling, sampling distributions, central limit theorem, sampling theorem, Sandler's A-test, estimation, sample size, hypothesis, hypothesis testing, Chi-square test, Yates correction, ANOVA, analysis of variance and co-variance, ANOVA technique, analysis of Co-variance, ANOCOVA, nonparametric test, distribution free test

Author: GA Adesina-Uthman, Okojie Daniel Esene

School: National Open University of Nigeria

Department: Administration, Social and Management science

Course Code: ECO356

Topics: Sampling Theory, Variance, Correlation, Econometrics, Random Variables, Sampling Theory, Covariance, Variance, Correlation, Regression Models, Hypothesis Testing, Dummy Variables.Simple Regression Analysis, Regression Coefficients, Multiple Regression Analysis, Multicollinearity, Transformations of Variables, regression variables, Heteroscedasticity/Heteroskedasticity, Autocorrelation, Error, Econometric Modelling, Stochastic Regression, measurement errors, Autocorrelation, Econometric Modelling, Time Series Data Models, Simultaneous Equation, Binary Choice, Maximum Likelihood Estimation