Ex.3: For experiment 2, Complete steps (a)-(c) of the checklist. Experiment 2: Does the boiling point of water differ with different concentrations of salt?

CheckList
(a)Define the objectives of the experiment
The purpose of this experiment is to determine the boiling point of water with different levels of concentration of salt.

(b)Identify all sources of variation
(i)Treatment factors and their levels
The treatment factor in this experiment is salt water of different concentrations. It can be chose to have three levels: low, medium, and high concentrations relative to the same amount of water. The levels are coded 1 for low, 2 for medium, and 3 for high.

(ii)Experimental units.
The experimental units will be the identical beakers and heaters that will be assigned to each level of salt water. The salt from the same package will be divided into three amounts represent three different levels low, regular, and high, and the weight of each amount will be weighted on a digital laboratory scale and should be corresponding to the required assigned amount for each level. The experiment should be done at the same place and the same time. Salt should be completely dissolved in water before heating, so salt water is also an experimental unit.

(iii)Blocking factors, noise factors, and covariates.
The blocking factors will be the types of salt, as each type of salt might be different in their composition, e.g. Producing area, treatments to the salt before packaging etc. Different composition of salt will have different solubility and thus result in a different boiling point. Apart from these differences in the composition of the salts, the amount of salt might not be as precise as the required amount, due to measurement errors. This also applies on the measurement of the amount of water for each level of salt, as well as the temperature of water.

(c)Choose a rule by which to assign the experimental...

...Trajico, Maria Liticia D.
BSEd III-A2
REFLECTION
The first thing that puffs in my mind when I heard the word STATISTIC is that it was a very hard subject because it is another branch of mathematics that will make my head or brain bleed of thinking of how I will handle it. I have learned that statistic is a branch of mathematics concerned with the study of information that is expressed in numbers, for example information about the number of times something happens. As I examined on what the statement says, the phrase “number of times something happens” really caught my attention because my subconscious says “here we go again the non-stop solving, analyzing of problems” and I was right. This course of basic statistic has provided me with the analytical skills to crunch numerical data and to make inference from it. At first I thought that I will be alright all along with this subject but it seems that just some part of it maybe it is because I don’t pay much of my attention to it but I have learned many things. I have learned my lesson.
During our every session in this subject before having our midterm examination I really had hard and bad times in coping up with this subject. When we have our very first quiz I thought that I would fail it but it did not happen but after that, my next quizzes I have taken I failed. I was always feeling down when in every quiz I failed because even though I don’t like this...

...of 1000 flights and proportions of three routes in the sample. He divides them into different sub-groups such as satisfaction, refreshments and departure time and then selects proportionally to highlight specific subgroup within the population. The reasons why Mr Kwok used this sampling method are that the cost per observation in the survey may be reduced and it also enables to increase the accuracy at a given cost.
TABLE 1: Data Summaries of Three Routes
Route 1
Route 2
Route 3
Normal(88.532,5.07943)
Normal(97.1033,5.04488)
Normal(107.15,5.15367)
Summary Statistics
Mean
88.532
Std Dev
5.0794269
Std Err Mean
0.2271589
Upper 95% Mean
88.978306
Lower 95% Mean
88.085694
N
500
Sum
44266
Summary Statistics
Mean
97.103333
Std Dev
5.0448811
Std Err Mean
0.2912663
Upper 95% Mean
97.676525
Lower 95% Mean
96.530142
N
300
Sum
29131
Summary Statistics
Mean
107.15
Std Dev
5.1536687
Std Err Mean
0.3644194
Upper 95% Mean
107.86862
Lower 95% Mean
106.43138
N
200
Sum
21430
From the table above, the total number of passengers for route 1 is 44,266, route 2 is 29,131 and route 3 is 21,430 and the total numbers of passengers for 3 routes are 94,827.
Although route 1 has the highest number of passengers and flights but it has the lowest means of passengers among the 3 routes. From...

...809000173
Question 2: Describe the relationship between research design and sample design
Before examining both sample design and research design it is important to be clear about the role and purposes of each design. Research design is a plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that a hypothesis can be tested properly. Every type of empirical research has a form of implicit research design. A design is a logical sequence that connects to empirical data to a study’s initial research question and ultimately it, to its conclusion. According to Adam G Bluman in his book Elementary Statistic define research design as a logical plan from getting from here to there, where here may be defined as initial questions to be answered , and there is some conclusion (answers) to these questions . Another text book has described research design as a plan that guides the investigator in the process of collecting analyzing and interpreting observations. It is a logical method of proof that allows the researcher to draw inreferences concerning causal relations among the variables under investigations (Nachmias andNachmaias 1992 pp. 77-78).
Research design can be seen as the “blue print” for your research, dealing with at least four problems what...

...to conception. The women were divided randomly into two groups. One group took daily multivitamins containing 0.8 mg of folic acid, whereas the other group received only trace elements. A drastic reduction in the rate of major birth defects occurred among the women who took folic acid: 13 per 1000 as compared to 23 per 1000 for those women who did not take folic acid.
a. Is this study an experiment? Explain your answer.
This is an experiment. It follows the 3 basic principles of experimental design:
1. Control: The experiment compares the group receiving the folic acid with the group receiving only a trace amount of the folic acid thereby reducing the effect of other factors in the women’s pregnancies on the outcome.
2. Randomization: The women were randomly assigned to one of the two groups.
3. Replication: The experiment was repeated for the 4753 women enrolled in the
experiment to reduce the effect of variation in the experiment.
b. Draw a diagram of the design of the randomized comparative experiment.
[pic]
c. What is the explanatory variable?
Amount of Folic Acid given to the subject
d. What is the response variable?
Number of Birth Defects
4a. Which of the following frequency polygons has a large positive skew?
Graph A has a positive skew.
b. Which has a large negative skew?
Graph C has a large negative skew.
[pic]
5. Suppose that the...

...Statistics 1
Business Statistics
LaSaundra H. – Lancaster
BUS 308 Statistics for Managers
Instructor Nicole Rodieck
3/2/2014
Statistics 2
When we hear about business statistics, when think about the decisions that a manager makes to help make his/her business successful. But do we really know what it takes to run a business on a statistical level? While some may think that businessstatistics is too much work because it entails a detailed decision making process that includes calculations, I feel that without educating yourself on the processes first you wouldn’t know how to imply statistics. This is a tool managers will need in order to run a successful business. In this paper I will review types of statistical elements like: Descriptive, Inferential, hypothesis development and testing and the evaluation of the results. Also I will discuss what I have learned from business statistics.
My description of Descriptive statistics is that they are the numerical elements that make up a data that can refer to an amount of a categorized description of an item such as the percentage that asks the question, “How many or how much does it take to “ and the outcome numerical amount. According to “Dr. Ashram’s Statistics site” “The quantities most commonly used to measure the dispersion of the values about...

...Organization of Terms
Experimental Design
Descriptive
Inferential
Population
Parameter
Sample
Random
Bias
Statistic
Types of
Variables
Graphs
Measurement scales
Nominal
Ordinal
Interval
Ratio
Qualitative
Quantitative
Independent
Dependent
Bar Graph
Histogram
Box plot
Scatterplot
Measures of
Center
Spread
Shape
Mean
Median
Mode
Range
Variance
Standard deviation
Skewness
Kurtosis
Tests of
Association
Inference
Correlation
Regression
Slope
y-intercept
Central Limit Theorem
Chi-Square
t-test
Independent samples
Correlated samples
Analysis-of-Variance
Glossary of Terms
Statistics - a set of concepts, rules, and procedures that help us to:
organize numerical information in the form of tables, graphs, and charts;
understand statistical techniques underlying decisions that affect our lives and well-being; and
make informed decisions.
Data - facts, observations, and information that come from investigations.
Measurement data sometimes called quantitative data -- the result of using some instrument to measure something (e.g., test score, weight);
Categorical data also referred to as frequency or qualitative data. Things are grouped according to some common property(ies) and the number of members of the group are recorded (e.g., males/females, vehicle type).
Variable - property of an object or event that can take on different values. For example,...

...techniques.
Firstly we look at data analysis. This approach starts with data that are manipulated or processed
into information that is valuable to decision making. The processing and manipulation of raw
data into meaningful information are the heart of data analysis. Data analysis includes data
description, data inference, the search for relationships in data and dealing with uncertainty
which in turn includes measuring uncertainty and modelling uncertainty explicitly.
In addition to data analysis, other decision making techniques are discussed. These techniques
include decision analysis, project scheduling and network models.
Chapter 1 illustrates a number of ways to summarise the information in data sets, also known as
descriptive statistics. It includes graphical and tabular summaries, as well as summary measures
such as means, medians and standard deviations.
Uncertainty is a key aspect of most business problems. To deal with uncertainty, we need a basic
understanding of probability. Chapter 2 covers basic rules of probability and in Chapter 3 we
discuss the important concept of probability distributions in some generality.
In Chapter 4 we discuss statistical inference (estimation), where the basic problem is to estimate
one or more characteristics of a population. Since it is too expensive to obtain the population
information, we instead select a sample from the population and then use the information in the
sample to infer the...

...central tendency of the sample.
6. Measures of dispersion: range, the interquartile range, the variance, and the standard deviation. What do these measures tell you about the “spread” of the data? Why is it important to spend time performing basic descriptive statistics prior to conducting inferential statistical tests?
Variance of a sample = S2 = =
Standard Deviation of sample S=
Range is the difference between the highest and the lowest values (250-100) = 150
Interquartile Range takes into consideration the fact that there are data extremes that affect the range. In the case of the data above, most of the values are around the median but two values (250 and 275) are extremes. In this scenario, Interquartile range is a better indication of the dispersion of the distribution
100 100 103 104 105 Q1 107 110 110 114 115 M 115 115 115 115 117 Q2 117 118 120 250 275
• Q1 = (105+107)/2 = 106
• Q2 = (117+117)/2 = 117
• IR = 117-106 =9
It is important to evaluate data and look at the entire picture to determine whether something fits or does not. The fact that we get two measurements that were extreme might be an indication that something may have gone wrong. Descriptive statistics in such a case becomes instrumental in our analysis
Type I and Type II Error: The concept of Type I and Type II Error is critical and will come into play with each statistical test you perform. Discuss the implications of...