variable presentation slideshare

• My presentations

Auth with social network:

Download presentation

We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!

Presentation is loading. Please wait.

Dependent and Independent variables explained

Published by Brook Chambers Modified over 6 years ago

Similar presentations

Presentation on theme: "Dependent and Independent variables explained"— Presentation transcript:

How to Write a Testable Question

Controls and Constants

Variables, Constants, and Controls

THIS WEEK Scientific Method. (Much). –Pick up worksheet (no sticker? You are missing vital info. Sticker = awesome). How can I help? –Also, Characteristics.

Dependent and Independent

Dependent and Independent Variables Explained Mr. Bushey.

Identifying Variables

Bell Ringer-“Do Now” Write five sentences to describe yourself.

Methods of Science Chapter 1.3 pages

The Scientific Method Movie Movie An experiment tests an idea in a careful orderly manner. The orderly steps used are called The Scientific Method.

Friday, September 9, 2011 Please follow the procedure for entering the room. Take out your Simpsons worksheet (homework from last night) and place it on.

1 The Scientific Method ntificmethod/

Scientific Method Lesson 2 1 Scientist _________________________________.

Did You Know?? If you can add 1+1 then you can learn today’s lesson about variables? = ? 2 - that’s right!!! Now you’re ready!

1. What are the steps (in order) of the scientific method? 2. Where do you think applying independent and dependent variables would fall in the scientific.

Graphs How can science be used to study people? 1.4 The student will construct appropriate charts, graphs, and tables to display data.

What is Science?.

Hypothesis?? Hypothesis: - It is an educated guess or prediction - It is an “if, then” statement - Keep you and I out of it.

Designing an experiment

Observation vs. Inference

About project

© 2024 SlidePlayer.com Inc. All rights reserved.

Types of variables

Mar 16, 2019

530 likes | 1.27k Views

Types of variables. Continuous can take on any value within a range (height, yield, etc.) measurements are approximate often normally distributed Discrete only certain values are possible (e.g., counts, scores) not normally distributed, but means may be Categorical

Share Presentation

• standard error
• confidence interval estimate
• soil type 1 produces

Presentation Transcript

Types of variables • Continuous • can take on any value within a range (height, yield, etc.) • measurements are approximate • often normally distributed • Discrete • only certain values are possible (e.g., counts, scores) • not normally distributed, but means may be • Categorical • qualitative; no natural order • often called classification variables • generally interested in frequencies of individuals in each class • binomial and multinomial distributions are common

Rounding and Reporting Numbers To reduce measurement error: • Standardize the way that you collect data and try to be as consistent as possible • Actual measurements are better than subjective readings • Minimize the necessity to recopy original data • Avoid “rekeying” data for electronic data processing • Most software has ways of “importing” data files so that you don’t have to manually enter the data again • When collecting data - examine out-of-line figures immediately and recheck

Significant Digits • Round means to the decimal place corresponding to 1/10th of the standard error (ASA recommendation) • Take measurements to the same, or greater level of precision • Maintain precision in calculations If the standard error of a mean is 6.96 grams, then 6.96/10 = 0.696  round means to the nearest 1/10th gram for example, 74.263  74.3 But if the standard error of a mean is 25.6 grams, then 25.6/10 = 2.56 round means to the closest gram for example, 74.263  74

Rounding in ANOVA • In doing an ANOVA, it is best to carry the full number of figures obtained from the uncorrected sum of squares If, for example, the original data contain one decimal, the sum of squares will contain two places 2.2 * 2.2 = 4.84 • Do not round closer than this until reporting final results

A B D A A B D C C D B C C D B A B A D C B A D C Experimental Design • An Experimental Design is a plan for the assignment of the treatments to the plots in the experiment • Designs differ primarily in the way the plots are grouped before the treatments are applied • How much restriction is imposed on the random assignment of treatments to the plots

Why do I need a design? • To provide an estimate of experimental error • To increase precision (blocking) • To provide information needed to perform tests of significance and construct interval estimates • To facilitate the application of treatments - particularly cultural operations

Factors to be Considered • Physical and topographic features • Soil variability • Number and nature of treatments • Experimental material (crop, animal, pathogen, etc.) • Duration of the experiment • Machinery to be used • Size of the difference to be detected • Significance level to be used • Experimental resources • Cost (money, time, personnel)

Cardinal Rule: • Choose the simplest experimental design that will give the required precision within the limits of the available resources

Completely Randomized Design (CRD) • Simplest and least restrictive • Every plot is equally likely to be assigned to any treatment A B D A C D B C B A D C

Advantages of a CRD • Flexibility • Any number of treatments and any number of replications • Don’t have to have the same number of replications per treatment (but more efficient if you do) • Simple statistical analysis • Even if you have unequal replication • Missing plots do not complicate the analysis • Maximum error degrees of freedom

A B D A C D B C B A D C Disadvantage of CRD • Low precision if the plots are not uniform

Uses for the CRD • If the experimental site is relatively uniform • If a large fraction of the plots may not respond or may be lost • If the number of plots is limited

Design Construction • No restriction on the assignment of treatments to the plots • Each treatment is equally likely to be assigned to any plot • Should use some sort of mechanical procedure to prevent personal bias • Assignment of random numbers may be by: • lot (draw a number ) • computer assignment • using a random number table

12 1 2 3 4 5 6 7 8 9 10 11 12 6 5 1 Random Assignment by Lot • We have an experiment to test three varieties: the top line from Oregon, Washington, and Idaho to find which grows best in our area ----- t=3, r=4 A A A A

Random Assignment by Computer (Excel) • In Excel, type 1 in cell A1, 2 in A2. Block cells A1 and A2. Use the ‘fill handle’ to drag down through A12 - or through the number of total plots in your experiment. • In cell B1, type = RAND(); copy cell B1 and paste to cells B2 through B12 - or Bn. • Block cells B1 - B12 or Bn, Copy; From Edit menu choose Paste special and select values (otherwise the values of the random numbers will continue to change)

Random numbers in Excel (cont’d.) • Sort columns A and B (A1..B12) by column B • Assign the first treatment to the first r (4) cells in column C, the second treatment to the second r (4) cells, etc. • Re-sort columns A B C by A if desired. (A1..C12)

The Statistical Analysis • Partitions the total variation in the data into components associated with sources of variation • For a Completely Randomized Design (CRD) • Treatments --- Error • For a Randomized Complete Block Design (RBD) • Treatments --- Blocks --- Error • Provides an estimate of experimental error (s2) • Used to construct interval estimates and significance tests • Provides a way to test the significance of variance sources

mean Yij =  + i + ij observation random error treatment effect Analysis of Variance (ANOVA) Assumptions • The error terms are… randomly, independently, and normallydistributed, with a mean of zero and a common variance. • The main effects are additive Linear additive model for a Completely Randomized Design (CRD)

The CRD Analysis We can: • Estimate the treatment means • Estimate the standard error of a treatment mean • Test the significance of differences among the treatment means

SiSj Yij=Y.. What? • i represents the treatment number (varies from 1 to t=3) • j represents the replication number (varies from 1 to r=4) • S is the symbol for summation Treatment (i) Replication (j) Observation (Yij) 1 1 47.9 1 2 50.6 1 3 43.5 1 4 42.6 2 1 62.8 2 2 50.9 2 3 61.8 2 4 49.1 3 1 66.4 3 2 60.6 3 3 64.0 3 4 64.0

grand mean mean of the i-th treatment deviation of the i-th treatment mean from the grand mean The CRD Analysis - How To: • Set up a table of observations and compute the treatment means and deviations

The CRD Analysis, cont’d. • Separate sources of variation • Variation between treatments • Variation within treatments (error) • Compute degrees of freedom (df) • 1 less than the number of observations • total df = N-1 • treatment df = t-1 • error df = N-t or t(r-1) if each treatment has the same r

Skeleton ANOVA for CRD

The CRD Analysis, cont’d. • Compute Sums of Squares • Total • Treatment • Error SSE = SSTot - SST • Compute Mean Squares • Treatment MST = SST / (t-1) • Error MSE = SSE / (N-t) • Calculate F statistic for treatments • FT = MST/MSE

Using the ANOVA • Use FT to judge whether treatment means differ significantly • If FT is greater than F in the table, then differences are significant • MSE = s2 or the sample estimate of the experimental error • Used to compute standard errors and interval estimates • Standard Error of a treatment mean • Standard Error of the difference between two means

Numerical Example • A set of on-farm demonstration plots were located throughout an agricultural district. A single plot was located within a lentil field on each of 20 farms in the district. • Each plot was fertilized and treated to control weevils and weeds. • A portion of each plot was harvested for yield and the farms were classified by soil type. • A CRD analysis was used to see if there were yield differences due to soil type.

1 2 3 4 5 42.2 28.4 18.8 41.5 33.0 34.9 28.0 19.5 36.3 26.0 29.7 22.8 13.1 31.7 30.6 18.5 10.1 31.0 19.4 28.2 Mean Mean 35.600 23.420 15.375 33.740 29.867 27.185 ri 3 5 4 5 3 20 Dev 8.415 -3.765 -11.810 6.555 2.682 Dev2 70.812 14.175 139.476 42.968 7.191 Table of Observations, Means, and Deviations

Source df SS MS F Total 19 1,439.2055 Soil Type 4 1,077.6313 269.4078 11.18** Error 15 361.5742 24.1049 ANOVA Table Fcritical(α=0.05; 4,15 df) = 3.06 ** Significant at the 1% level

Formulae and Computations Coefficient of Variation Standard Error of a Mean Confidence Interval Estimate of a Mean (soil type 4)

Formulae for Mean Comparisons Standard Error of the Difference between Two Means (for soils 1 and 2) Test statistic with N-t df

Mean Yields and Standard Errors Soil Type 1 2 3 4 5 Mean Yield 35.60 23.42 15.38 33.74 29.87 Replications 3 5 4 5 3 Standard error 2.83 2.20 2.45 2.20 2.83 CV = 18.1% 95% confidence interval estimate for soil type 4 = 33.74 4.69 Standard error of difference between 1 and 2 = 3.58

1 2 4 5 3 Report of Analysis • Analysis of yield data indicates highly significant differences in yield among the five soil types • Soil type 1 produces the highest yield of lentil seed, though not significantly different from type 4 • Soil type 3 is clearly inferior to the others

• More by User

Types of Variables

Types of Variables. AP Statistics 06-07 Dobson. Review of Terms. Individual: The objects described by a set of data, individuals may be people, animals or things Ex: Students in an AP Statistics class

629 views • 9 slides

Types of units and variables

Types of units and variables. Examples of variables. What are the possible units?. Murder rate Litigation rate Support for freedom of speech Income Party identification Liberalism. What is not a variable?. Speed of light Parameters Statistics These are called constants.

775 views • 62 slides

Types of Variables. Objective: Students should be able to identify the different types of variables, and know the characteristics of each type. Types of Variables. Categorical (data that are counted) Nominal Ordinal Quantitative or Numerical (data that are measured) Interval Ratio.

3.43k views • 13 slides

Abstract Types Defined as Classes of Variables

Abstract Types Defined as Classes of Variables. Jeffrey Smith, Vincent Fumo, Richard Bruno. Introduction. What is a type Current approaches Why a new definition The new approach Common Types Questions. What is a Type?.

469 views • 31 slides

Types of Variables:

Types of Variables:. Numerical vs. non-numerical Discrete vs. continuous A very special variable = “binary” variables What is a “binary variable? Why are they interesting or important? What are their important characteristics? What statistics apply to binary variables?.

221 views • 5 slides

Variables and Data Types

Section 3.2. Variables and Data Types. Variables hold data that may be manipulated, are used to manipulate other data, or can remember data for later use. Why Have Variables?.

796 views • 20 slides

Types, Variables and Operators

Types, Variables and Operators. Computer Engineering Department Java Course Asst. Prof. Dr. Ahmet Sayar Kocaeli University - Fall 201 3. Types. Kinds of values that can be stored and manipulated boolean : Truth value (true or false). int: Integer (0, 1, -47).

395 views • 26 slides

Types of Variables. 6 th , 7 th , & 8 th Science Mrs. Prince’s Class 2013-2014. A variable is……. Anything that can affect the outcome of an experiment. Independent Variable (Manipulated Variable). The tested variable. The one thing that you will change in your experiment.

306 views • 7 slides

Variables and data types

Variables and data types. Chapter2:part1. Objectives:. By the end of this section you should: Understand what the variables are and why they are used. Use C++ built in data types to create program variables. Apply C++ syntax rules to declare variables, initialize them.

2.31k views • 22 slides

Variables and Data Types. Data (information we're going to store) Numbers Text Dates What types of data can JavaScript process? How do we store it? How do we use it in code?. Data Types. Common: Dates, Text, Numbers Other, more abstract, data types boolean, etc.

804 views • 25 slides

Expressions, Statements, Variables, Assignments, Types

Expressions, Statements, Variables, Assignments, Types. CSE 1310 – Introduction to Computers and Programming Vassilis Athitsos University of Texas at Arlington Credits: a significant part of this material has been created by Dr. Darin Brezeale and Dr. Gian Luca Mariottini. Expression.

599 views • 47 slides

Constants, Variables and Data Types

Constants, Variables and Data Types. Mr Henry . Starter:. X=6, Y =7, A= 10 X+Y= A-Y= Y+E=12 What is E?. Variables. What is a variable? Research and describe to a partner. A variable is something which can change! (It varies!!). Paired Work!.

242 views • 8 slides

Variables and Data Types. Objectives of this session. Keywords Identifiers Basic Data Types bool & wchar_t Built-in Data Types User-defined Data Types Derived Data Types Symbolic Constants Dynamic Initialization of Variables Reference Variables. Variables and Data Types. Tokens

922 views • 30 slides

Data Types, Variables, and Arrays

Data Types, Variables, and Arrays. Java Is a Strongly Typed Language. Every variable has a type, every expression has a type, and every type is strictly defined All assignments, whether explicit or via parameter passing in method calls, are checked for type compatibility

1.79k views • 55 slides

LINGUISTIC VARIABLES. Types of variables. Lexical - vocabulary. Grammatical. Phonological - pronunciation. Types of variables. Lexical - vocabulary. This and the following maps are from Widdowson and Upton. Isoglosses showing lexical variables. Isoglosses showing lexical

588 views • 36 slides

Data Types and Variables

Data Types and Variables. Doncho Minkov. Telerik Software Academy. http://academy.telerik.com. Technical Trainer. http://minkov.it. Table of Contents. Data Types Integer Floating-Point Boolean String Declaring and Using Variables Identifiers

662 views • 38 slides

Types of variables, treatment of missing data

Types of variables, treatment of missing data. 20.10. 2009. Types of variables (number of values). continuous variables (any value within a range) discrete variables (finite number of values) some discrete variables can be treated as continuous (counts). Types of variables (measurment).

367 views • 15 slides

Variables & Data Types

Variables & Data Types. Storing and naming data. Working with data. Data must be loaded into main memory before it can be manipulated Store process: Allocate memory Store data in the allocated memory. Declaring Variables. Variable : memory location whose content may change

689 views • 34 slides

Abstract Types Defined as Classes of Variables. D. L. Parnas, J.E. Shore, D.M. Weiss. Abstract Data Types. Defining Data Types is Important Scalar types are somewhat consistently defined for a programming language, and are precisely defined for a given compiler and runtime environment.

208 views • 19 slides

What a C++ program looks like. Variables Declaration of variables Types of variables

Lecture 2: Introduction to C++ Programs. What a C++ program looks like. Variables Declaration of variables Types of variables assigning values to variables Names of variables “for” statement (Syntax and Examples) “if” statement (Syntax and Examples)

401 views • 36 slides

Types of Variables in a Scientific Experiment

Types of Variables in a Scientific Experiment. Cornell Notes Page 35. Independent Variable (IV) Dependent Variable (DV). The “Cause” Variable being tested “I change” The “Effect” Variable being measured “depends” on independent variable. Constants/ Controlled Variables ( C ).

79 views • 4 slides

Other types of variables

Other types of variables. double (also known as “floating point number”) allows decimals much more useful than integers, but takes up more memory char A single character You must put the character in single quotes , if you are giving a char value in your code

212 views • 16 slides