1. what are the three mechanisms of runoff generation in a watershed? 2. Given a unit hyrograph and a constant precipitation event (lasting only a single time unit), how do you determine the direct runoff hydrograph? What information do you need to then determine the total streamflow hydrograph? 3. Given a unit hyrograph and a time varying precipitation event, how do you determine the direct runoff hydrograph? 4. How do you calculate a unit hydrograph given a total streamflow hydrograph and a time varying precipitation event? What other information do you need besides the total streamflow hydrograph and the hyetograph to do this calculation? Solution Q.1 Answer Streamflow is generated by three mechanisms 1.Horton overland flow 2.Subsurface flow 3.Saturation overland flow Horton overland flow : # Horton overland flow describes the tendency of water to flow horizontally across land surfaces when rainfall has exceeded infiltration capacity and depression storage capacity. Subsurface flow : # Subsurface flow is the flow of water beneath earth\'s surface as part of the water cycle when rainfall occurs and water infiltrates through the surface of land Saturation overland flow # In many regions runoff is most commonly generated on relatively small portions of the landscape that are susceptible to becoming completely saturated. # Once the soils in these areas saturate to the surface, any additional rainfall that falls becomes overland flow. This process is termed saturation-excess overland flow.

1. 1. What is a population? What is a sample?
2. What is descriptive statistics? What is inferential statistics?
3. What is sampling error?
4. What are the four levels of measurement? What do we mean when we say that some variables
are categorical and some are continuous?
5. What do measures of central tendency tell us about a group of scores? What is the mean?
What
is the median? What is the mode? How do you know which measure of central tendency to use?
How do measures of central tendency relate to the shape of a distribution of a continuous
variable?
6. What do measures of variability tell us about a group of scores? What is the relationship
between variance and standard deviation? How do we calculate standard deviation?
7. What is a construct? What is an operationalization? What is validity and reliability?
8. How do we calculate the probability of an event occurring? What is the normal curve? How
does the normal curve help us calculate probability?
9. What are independent events? What are mutually exclusive events? When are events
complementary?
10. What is the distribution of sample means (also known as a sampling distribution)? What is
the
expected value of M and how do we calculate it? What is the standard error and how do we
calculate it?How does standard error relate to sampling error?
11. How do we calculate z for an individual score? How do we calculate z for a sample mean?
12. What are the four steps of hypothesis testing?
13. What is Type I error? How do we measure the risk of Type I error? What is Type II error?
14. What is the null hypothesis? What are the null hypotheses for each of the different types of
tests we%u2019ve run in class? Why do we focus on the null hypothesis in hypothesis testing?
15. What are confidence intervals and how do we interpret them?
16. Under what conditions do we use t instead of z in a hypothesis test? When do we use each of
the three types of t tests?
17. What are the advantages of using a repeated-measures t test over an independent-measures t
test? What are the disadvantages?
18. When do we use ANOVA instead of t tests?
19. What are post-hoc tests and when do we use them?20. Why is a correlational relationship
between two variables not the same thing as a cause-andeffect relationship?
21. What two things does Pearson%u2019s rtell you about the relationship between two

2. variables?
22. What does regression allow you to do that correlation does not?
23. What level of measurement do the variables need to be on for t tests? ANOVA? Chi-square?
Correlations/regression?
24. What are the appropriate effect size calculations for each of the tests we%u2019ve covered
this
semester?What are the scales for each of the effect size calculations?
Solution
1. A population is a collection of data whose properties are analyzed. The population is the
complete collection to be studied, it contains all subjects of interest. A sample is a part of the
population of interest, a sub-collection selected from a population.
2. Descriptive statistics is the discipline of quantitatively describing the main features of a
collection of data, or the quantitative description itself.
Mathematical methods that employ probability theory for deducing (inferring) the properties of a
population from the analysis of the properties of a data sample drawn from it. It is concerned also
with the precision and reliability of the inferences it helps to draw.
Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that
descriptive statistics aim to summarize a sample, rather than use the data to learn about the
population that the sample of data is thought to represent.
3. In statistics, sampling error is incurred when the statistical characteristics of a population are
estimated from a subset, or sample, of that population. Since the sample does not include all
members of the population, statistics on the sample, such as means and quantiles, generally differ
from parameters on the entire population.
4. Four Levels of Measurment ->
Nominal
Ordinal

3. Interval
Ratio
Categorical variables are also known as discrete or qualitative variables. Categorical variables
can be further categorized as either nominal, ordinal or dichotomous.
Nominal variables are variables that have two or more categories, but which do not have an
intrinsic order. For example, a real estate agent could classify their types of property into distinct
categories such as houses, condos, co-ops or bungalows. So "type of property" is a nominal
variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different
categories of a nominal variable can also be referred to as groups or levels of the nominal
variable. Another example of a nominal variable would be classifying where people live in the
USA by state. In this case there will be many more levels of the nominal variable (50 in fact).
Dichotomous variables are nominal variables which have only two categories or levels. For
example, if we were looking at gender, we would most probably categorize somebody as either
"male" or "female". This is an example of a dichotomous variable (and also a nominal
variable). Another example might be if we asked a person if they owned a mobile phone. Here,
we may categorise mobile phone ownership as either "Yes" or "No". In the real estate agent
example, if type of property had been classified as either residential or commercial then "type of
property" would be a dichotomous variable.
Ordinal variables are variables that have two or more categories just like nominal variables only
the categories can also be ordered or ranked. So if you asked someone if they liked the policies
of the Democratic Party and they could answer either "Not very much", "They are OK" or
"Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories, namely
"Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most
positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much).
However, whilst we can rank the levels, we cannot place a "value" to them; we cannot say that
"They are OK" is twice as positive as "Not very much" for example.