I am relatively new to the world of programming and I was wondering if anybody could help me with a small project. I am trying to create two programs in VB.Net that each do one of the following individual actions:
Find the average grade given several user-inputted scores on assignments. The program should also provide the following feedback according to the final score (i.e. A, B, C, D, F).
Run two separate threads printing numbers (or words) in ascending and descending orders. (The numbers (or words) should be given by the user.)
I have a basic understanding of VB.Net, but I am having trouble when it comes to creating even remotely complex programs. I have a few ideas on how I may go about these, such as using an arraylist for the first question that takes user input to find the grades, and then uses a series of If-Then-Else statements to display the letter grade, and possibly using steps simply with dual threading that would result in numerical order being printed in ascending order and descending order. Any help or advice would be greatly appreciated.
P.S.
I will be adding the code I have so far for both of these programs shortly. In the meantime, if you can help me at all with the information I have given you, it would be helpful.
Here's some pseudocode to help you get started on #1:
get list of scores from user input (Console.Readline() if this is a console app)
assign scores to an array or list (List would be a good choice)
get the total score (assuming they're not weighted, if you have a List then you can just use the Sum() method)
get the number of grades (again, if you have a List then you can use Count() method)
divide total by count to get average (if you need a decimal average, you'll have to cast your values to double first, or if your average can be an int just leave them all as int)
use an if-then-else to compare the average to each grade cutoff (if > 90 then "A", else if > "80" then "B", etc)
Related
I have a tableau table as follows:
This data can be visualized as follows:
I'd like to flag cases that have lumps/clusters. This would flag items B, C and D because there are spikes only in certain weeks of the 13 weeks. Items A and E would not be flagged as they mostly have a 'flat' profile.
How can I create such a flag in Tableau or SQL to isolate this kind of a case?
What I have tried so far?:
I've tried a logic where for each item I calculate the MAX and MEDIAN. Items that need to be flagged will have a larger (MAX - MEDIAN) value than items that have a fairly 'flat' profile.
Please let me know if there's a better way to create this flag.
Thanks!
Agree with the other commenters that this question could be answered in many different ways and you might need a PhD in Stats to come up with an ideal answer. However, given your basic requirements this might be the easiest/simplest solution you can implement.
Here is what I did to get here:
Create a parameter to define your "spike". If it is going to always be a fixed number you can hardcode this in your formulas. I called min "Min Spike Value".
Create a formula for the Median Values in each bucket. {fixed [Buckets]: MEDIAN([Values])} . (A, B, ... E = "Buckets"). This gives you one value for each letter/bucket that you can compare against.
Create a formula to calculate the difference of each number against the median. abs(sum([Values])-sum([Median Values])). We use the absolute value here because a spike can either be negative or positive (again, if you want to define it that way...). I called this "Spike to Current Value abs difference"
Create a calculated field that evaluates to a boolean to see if the current value is above the threshold for a spike. [Spike to Current Value abs difference] > min([Min Spike Value])
Setup your viz to use this boolean to highlight the spikes. The beauty of the parameter is you can change the value for what a spike should be and it will highlight accordingly. Above the value was 4, but if you change it to 8:
Good afternoon and happy Friday, folks
I’m trying to automate a placement simulation of youth into residential treatment where they will have the highest likelihood of success. Success is operationalized as “not recidivating” within 3 years of entering treatment. Equations predicting recidivism have been generated for each location, and the equations have been applied to each individual in the scenario (based on youth characteristics like risk, age, etc., LOS). Each youth has predicted success rates for every location, which throws in a wrench: youth are not qualified for all of the treatment facilities for which they have predicted success rates. Indeed, treatment locations have differing, yet overlapping qualifications.
Let’s take a made-up example. Johnny (ID # 5, below) is a 15-year-old boy with drug charges. He could have “predicted success rates” of 91% for location A, 88% for location B, 50% for location C, and 75% for location D. Johnny is most likely to be successful (i.e., not recidivate within three years of entering treatment) if he is treated at location A; unfortunately, location A only accepts youth who are 17 years old or older; therefore, Johnny would not qualify for treatment here. Alternatively, for Johnny, location B is the next best location. Let us assume that Johnny is qualified for location B, but that all of location-B beds are filled; so, we must now look to location D, as it is now Johnny’s “best available” option at 75%.
The score so far: We are matching youth to available beds in location for which they qualify and might enjoy the greatest likelihood of success. Unfortunately, each location only has a certain number of available beds, and the number of available beds different across locations. The qualifications of entry into treatment facilities differ, yet overlap (e.g., 12-17 year-olds vs 14-20 year-olds).
In order to simulate what placement decisions might look like based on success rates, I went through the scenario describe above for over 400 youth, by hand, in excel. It took me about a week. I’d like to use PROC SQL imbedded in a SAS MACRO to automate these placement scenarios with the ultimate goals of a) obtain the ability to bootstrap iterations in order to examine effect sizes across distributions, b) save time, and c) prevent further brain damage from banging my head again desk and wall in frustration whilst doing this by hand. Whilst never having had the necessity—nay—the privilege of using SQL in my typical roll as a researcher, I believe that this time has now come to pass and I’m excited about it! Honestly. I believe it has the capacity I’m looking for. Unfortunately, it is beating the devil out of me!
Here’s what I’ve got cookin’ so far: I want to create and automate the placement simulation with the clever use of merging/joining/switching/or something like that.
I have two datasets (tables). The first dataset contains all of the youth information (one row per youth; several columns with demographics, location ranks, which correspond to the predicted success rates). The order of rows in the youth dataset (was/will be randomly generated (to simulate the randomness with which youth enter the system and are subsequently place into treatment). Note that I will be “cleaning” the youth dataset prior to merging such that rank-column cells will only be populated for programs for which a respective youth qualifies. This should take the “does the youth even qualify for the program” problem out of the equation.
However, it still leaves the issue of availability left to be contended with in the scenario.
The second dataset containing the treatment facility beds, with each row corresponding to an available bed in one of the treatment location; two columns contain bed numbers and location names. Each bed (row) has only one location cell populated, but locations will populate several cells.
Thus, in descending order, I want to merge each youth row with the available bed that represents his/her best chance of success, and so the merge/join/switch/thing should take place
on youth.Rank1= distinct TF.Location,
and if youth.Rank1≠ TF.location then
merge on youth.Rank2= TF.location,
if youth.Rank2≠ TF.location then merge at
youth.Rank3 = TF.location, etc.
Put plainly: “Merge on rank1 unless rank1 location is no longer available, then merge on rank2, unless rank2 location is no longer available, and on down the line, etc., etc., until all option are exhausted and foster care (i.e., alternative services). Is the only option.
I’ve had no success getting this to work. I haven’t even been successful getting the union function to work. About the only successful thing I’ve done in SQL so far is create a view of a single dataset. It’s pretty sad. I’ve been following this guidance, but I get hung up around the “where” command:
proc sql; /Calls the SQL procedure*/;
create table x as /*Tells SAS to create a table called x*/
select /*Specifies the column(s) to be selected*/
from /*Specificies the tables(s) (data sets) to be queried*/
where /*Subjests the data based on a condition*/
group by /*Classifies the data into groups based on the specified
column(s)*/
order by /*Sorts the resulting rows observations) by the specified
column(s)*/
; quit; /*Ends the proc sql procedure*/
Frankly, I’m stuck and I could use some advice. This greenhorn in me is in way over his head.
I appreciate any help or guidance anyone might lend.
Cheers!
P
The process you describe (and to be honest I skiped to the end so I might of missed something) does not lend itself to SQL because each step could affect the results of the next one. However, you want to get the most best results for the most kids. (I think a lot of that text was to convince us how important it is to help out). You don't actually give us anything we can really use to help since you don't give any details of your data model, your data, or expected results. There really is no way to answer this question. But I don't care -- I'm going to go forward with some suggestions because it is a friday and I've never done a stream of consciousness answer to a stream of consciousness question before. I will suggest you don't formulate your solution just in sql, but instead use a higher level program and engage is a process like the one described below -- because this a DB questions I've noted the locations where the DB might be involved.
Generate a list kids (this can be in a table -- called NEEDY-KID)
Have a list of locations to assign (this can also be a table LOCATION)
Run your matching for best fit from KID to location -- at this point don't worry about assign more than one kid to a location -- there can be duplicates (put this in table called KID2LOC using a query)
Check KID2LOC for locations assigned twice -- use some method to remove the duplicate ones so each loc is only assigned once. (remove from the KID2LOC using a query)
Prune the LOCATION list to remove assigned locations (once again -- a query)
If kids exist without a location go to 3 with new pruned location list.
Done.
currently we're building a database to track different factories' pollutant emissions. Now a query is needed that gives us information about relative quantities. Somehow I feel this should be straight forward but I have had no success implementing it in SQL.
I'm starting from a working query that returns the following fields:
PRODUCTION_YEAR, COMPANY, PRODUCT_CATEGORY, POLLUTANT, TOTAL_EMISSIONS, SHARE
TOTAL_EMISSIONS contains the total emissions for each company in a particular year and product category. SHARE is a computed field and contains the contribution (as a fraction) of each company to that year's overall emissions of that particular pollutant in that particular product category.
Now the task is to count the factories contributing to each pollutant. I arrived at this:
SELECT PRODUCTION_YEAR, POLLUTANT, PRODUCT_CATEGORY, Count(COMPANY)
FROM theQuery
GROUP BY PRODUCTION_YEAR, POLLUTANT, PRODUCT_CATEGORY;
However, now our client wants something more sophisticated: count only the biggest polluters who contribute 95% of emissions. In a script, I'd probably just have the pollution percentages in each category sorted ascendingly, then walk the dataset, sum up the shares and only start counting after reaching 5%. Doing it in SQL, no idea.
My first step (adding a SUM(SHARE) field to the new query) already resulted in errors ("expression not included in aggregate function", roughly translated, not sure what to make of it because all the expressions were indeed included). Is there even a way to do this in an SQL query, or am I wasting my time and would be better off just writing some VBA?
Thanks for any input!
Best,
Ben
Gord's method (see link in comment) works well for this task.
I'd like your advices regarding optimalization of this:
Data:
I have SQLite database with +- 3000 cities, all of which have name and some lattitude and longitude. All cities have also relevance (based on how often user visits them). Relevance is classic integer. Then, I have user location, again, as lat/lon coordinates.
Request:
I need to create autocomplete editBox. Suggestions must satisfy these conditions:
1) Phrase in editBox must be a substring of suggested city name.
2) Suggestions must by ordered first by relevance. (Classic integer ordering, no problem)
3) If relevance is the same, then suggestions are ordered by distance to user.
4) Display max. 10 suggestions.
Since there are usually a lot of cities with equal relevance, biggest problem is the distance ordering.
My current approach:
A) Get IDs and coordinates of cities that satisfy condition (1) and (2) using classic: name LIKE '% phrase%' ordered by relevance.
B) Split result to groups by relevance. Order these relevance groups by distance using sorting in Java.
C) When there are 10 suggestions that are fixed, (f.e. 11 relevance groups, all containing one city, so no location ordering is needed) stop ordering.
This works well. But, there is a problem. Usually, very few cities have different relevance.
So when user starts typing and there is just one or two letters in the search phrase, I end up sorting 500 cities by distance, just to get to my 10 suggestions, what I find highly inefficient.
Is there any better way to handle such situations using SQLite?
P.S. It is running on Android, if that helps :)
Let's say I have a table with 10.000 lines (representing 10.000 persons) and the following columns:
id qualification gender age income
When I select all persons having a certain qualification (say "plumber") I get 100 lines, having a certain gender, age and income distribution.
What I now want to do is select some kind of test group to check if the income is influenced by qualification or by the distribution of the other attributes.
That means (and now I come to my question) I want to get another set of 100 lines, having the same gender and age distribution (but a different qualification value). These 100 lines should of course been chosen by random.
My primary problem is that I don't know how to write an SQL command that would take care of the distributions (which of course could and maybe should be seen as probabilities in this context) when I select random lines.
Thank you in advance!
You seem to be trying to solve something that is tightly related to this extremely thorny problem.
The wiki page depicts a number of approaches for detecting correlations in a database, complete with references to prior pg-hacker discussions (here's another), a variety of (rejected) patch proposals, and scientific papers that discusses the topic.
If it sounds too thorny, I'd second Catcall's pl/r suggestion. Or another applicable pl, for that matter.
As an aside, you might find pg-kmeans useful too:
http://pgxn.org/dist/kmeans/doc/kmeans.html
As well as PostStat (never tried it myself):
http://poststat.projects.postgresql.org/
Might be better on stats.stackexchange.com.
Selecting random rows is easy; matching the distribution is hard.
You could write a stored procedure that
repeatedly selects 100 rows at random,
calculates the statistics,
and returns when it finds 100 rows that fit.
But that seems a lot like kicking dead whales down the beach. And, depending on your data, it might never return.
Before you spend much time trying to do this in SQL, consider spending a little time to see how hard (or how easy) this is to do with statistical software, like R.
Later
Just discovered that there's a package called pl/R.
PL/R is a loadable procedural language that enables you to write
PostgreSQL functions and triggers in the R programming language. PL/R
offers most (if not all) of the capabilities a function writer has in
the R language.
Google postgresql +statistics +r +pl for additional links to papers and tutorials.
SELECT * from Table1 order by random() limit 100;
random() is valid for PostgreSql. For MySql you can use RAND() instead of Random()